38-Issue 2

Permanent URI for this collection

Procedural Modeling
Procedural Tectonic Planets
Yann Cortial, Adrien Peytavie, Eric Galin, and Eric Guérin
Local Editing of Procedural Models
Markus Lipp, Matthias Specht, Cheryl Lau, Peter Wonka, and Pascal Müller
String-Based Synthesis of Structured Shapes
Javor Kalojanov, Isaak Lim, Niloy Mitra, and Leif Kobbelt
Meshing and Geometry Processing
Dual Sheet Meshing: An Interactive Approach to Robust Hexahedralization
Kenshi Takayama
Fluids
A Geometrically Consistent Viscous Fluid Solver with Two-Way Fluid-Solid Coupling
Tetsuya Takahashi and Ming C. Lin
Deep Fluids: A Generative Network for Parameterized Fluid Simulations
Byungsoo Kim, Vinicius C. Azevedo, Nils Thuerey, Theodore Kim, Markus Gross, and Barbara Solenthaler
Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow
Steffen Wiewel, Moritz Becher, and Nils Thuerey
Styles and Fonts
StyleBlit: Fast Example-Based Stylization with Local Guidance
Daniel Sýkora, Ondrej Jamriška, Ondrej Texler, Jakub Fišer, Mike Lukác, Jingwan Lu, and Eli Shechtman
Rendering Systems
Hierarchical Rasterization of Curved Primitives for Vector Graphics Rendering on the GPU
Mark Dokter, Jozef Hladky, Mathias Parger, Dieter Schmalstieg, Hans-Peter Seidel, and Markus Steinberger
Parameterization and Correspondences
A Subspace Method for Fast Locally Injective Harmonic Mapping
Eden Fedida Hefetz, Edward Chien, and Ofir Weber
Elastic Correspondence between Triangle Meshes
Danielle Ezuz, Behrend Heeren, Omri Azencot, Martin Rumpf, and Mirela Ben-Chen
Exact Constraint Satisfaction for Truly Seamless Parametrization
Manish Mandad and Marcel Campen
Sampling
A Low-Dimensional Function Space for Efficient Spectral Upsampling
Wenzel Jakob and Johannes Hanika
Accurate Synthesis of Multi-Class Disk Distributions
Pierre Ecormier-Nocca, Pooran Memari, James Gain, and Marie-Paule Cani
Learning to Importance Sample in Primary Sample Space
Quan Zheng and Matthias Zwicker
Humans in Motion
Character Navigation in Dynamic Environments Based on Optical Flow
Axel López, François Chaumette, Eric Marchand, and Julien Pettré
Videos
Deep HDR Video from Sequences with Alternating Exposures
Nima Khademi Kalantari and Ravi Ramamoorthi
Clear Skies Ahead: Towards Real-Time Automatic Sky Replacement in Video
Tavi Halperin, Harel Cain, Ofir Bibi, and Michael Werman
Deep Video-Based Performance Cloning
Kfir Aberman, Mingyi Shi, Jing Liao, Dani Lischinski, Baoquan Chen, and Daniel Cohen-Or
Learning to Render
Neural BTF Compression and Interpolation
Gilles Rainer, Wenzel Jakob, Abhijeet Ghosh, and Tim Weyrich
Gradient Outlier Removal for Gradient-Domain Path Tracing
Saerom Ha, Sojin Oh, Jonghee Back, Sung-Eui Yoon, and Bochang Moon
Better Patterns
Generating Stochastic Wall Patterns On-the-fly with Wang Tiles
Alexandre Derouet-Jourdan, Marc Salvati, and Théo Jonchier
Generating Color Scribble Images using Multi-layered Monochromatic Strokes Dithering
Yi-Hsiang Lo, Ruen-Rone Lee, and Hung-Kuo Chu
Fabrication
Multi-Pose Interactive Linkage Design
Gen Nishida, Adrien Bousseau, and Daniel G. Aliaga
Computational Design of Steady 3D Dissection Puzzles
Keke Tang, Peng Song, Xiaofei Wang, Bailin Deng, Chi-Wing Fu, and Ligang Liu
Object Partitioning for Support-Free 3D-Printing
Eli Karasik, Raanan Fattal, and Michael Werman
Modeling
Dynamic Visibility-Driven Molecular Surfaces
Stefan Bruckner
Geometry Aware Tori Decomposition
Jia Chen and Meenakshisundaram Gopi
Design and Automated Generation of Japanese Picture Puzzles
Mees van de Kerkhof, Tim de Jong, Raphael Parment, Maarten Löffler, Amir Vaxman, and Marc van Kreveld
Learning to Animate
Learning-Based Animation of Clothing for Virtual Try-On
Igor Santesteban, Miguel A. Otaduy, and Dan Casas
Learning a Generative Model for Multi-Step Human-Object Interactions from Videos
He Wang, Sören Pirk, Ersin Yumer, Vladimir G. Kim, Ozan Sener, Srinath Sridhar, and Leonidas J. Guibas
Latent-space Dynamics for Reduced Deformable Simulation
Lawson Fulton, Vismay Modi, David Duvenaud, David I. W. Levin, and Alec Jacobson
Learning Images
Controlling Motion Blur in Synthetic Long Time Exposures
Marcel Lancelle, Pelin Dogan, and Markus Gross
What's in a Face? Metric Learning for Face Characterization
Omry Sendik, Dani Lischinski, and Daniel Cohen-Or
Exploratory Stage Lighting Design using Visual Objectives
Evan Shimizu, Sylvain Paris, Matthew Fisher, Ersin Yumer, and Kayvon Fatahalian
Flow and Rigs
A CNN-based Flow Correction Method for Fast Preview
Xiangyun Xiao, Hui Wang, and Xubo Yang
Practical Person-Specific Eye Rigging
Pascal Bérard, Derek Bradley, Markus Gross, and Thabo Beeler

BibTeX (38-Issue 2)
                
@article{
10.1111:cgf.13651,
journal = {Computer Graphics Forum}, title = {{
EUROGRAPHICS 2019: CGF 38-2 Frontmatter}},
author = {
Alliez, Pierre
and
Pellacini, Fabio
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13651}
}
                
@article{
10.1111:cgf.13614,
journal = {Computer Graphics Forum}, title = {{
Procedural Tectonic Planets}},
author = {
Cortial, Yann
and
Peytavie, Adrien
and
Galin, Eric
and
Guérin, Eric
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13614}
}
                
@article{
10.1111:cgf.13615,
journal = {Computer Graphics Forum}, title = {{
Local Editing of Procedural Models}},
author = {
Lipp, Markus
and
Specht, Matthias
and
Lau, Cheryl
and
Wonka, Peter
and
Mueller, Pascal
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13615}
}
                
@article{
10.1111:cgf.13616,
journal = {Computer Graphics Forum}, title = {{
String-Based Synthesis of Structured Shapes}},
author = {
Kalojanov, Javor
and
Lim, Isaak
and
Mitra, Niloy
and
Kobbelt, Leif
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13616}
}
                
@article{
10.1111:cgf.13617,
journal = {Computer Graphics Forum}, title = {{
Dual Sheet Meshing: An Interactive Approach to Robust Hexahedralization}},
author = {
Takayama, Kenshi
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13617}
}
                
@article{
10.1111:cgf.13618,
journal = {Computer Graphics Forum}, title = {{
A Geometrically Consistent Viscous Fluid Solver with Two-Way Fluid-Solid Coupling}},
author = {
Takahashi, Tetsuya
and
Lin, Ming C.
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13618}
}
                
@article{
10.1111:cgf.13619,
journal = {Computer Graphics Forum}, title = {{
Deep Fluids: A Generative Network for Parameterized Fluid Simulations}},
author = {
Kim, Byungsoo
and
Azevedo, Vinicius C.
and
Thuerey, Nils
and
Kim, Theodore
and
Gross, Markus
and
Solenthaler, Barbara
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13619}
}
                
@article{
10.1111:cgf.13620,
journal = {Computer Graphics Forum}, title = {{
Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow}},
author = {
Wiewel, Steffen
and
Becher, Moritz
and
Thuerey, Nils
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13620}
}
                
@article{
10.1111:cgf.13621,
journal = {Computer Graphics Forum}, title = {{
StyleBlit: Fast Example-Based Stylization with Local Guidance}},
author = {
Sýkora, Daniel
and
Jamriška, Ondrej
and
Texler, Ondrej
and
Fišer, Jakub
and
Lukác, Mike
and
Lu, Jingwan
and
Shechtman, Eli
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13621}
}
                
@article{
10.1111:cgf.13622,
journal = {Computer Graphics Forum}, title = {{
Hierarchical Rasterization of Curved Primitives for Vector Graphics Rendering on the GPU}},
author = {
Dokter, Mark
and
Hladký, Jozef
and
Parger, Mathias
and
Schmalstieg, Dieter
and
Seidel, Hans-Peter
and
Steinberger, Markus
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13622}
}
                
@article{
10.1111:cgf.13623,
journal = {Computer Graphics Forum}, title = {{
A Subspace Method for Fast Locally Injective Harmonic Mapping}},
author = {
Hefetz, Eden Fedida
and
Chien, Edward
and
Weber, Ofir
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13623}
}
                
@article{
10.1111:cgf.13624,
journal = {Computer Graphics Forum}, title = {{
Elastic Correspondence between Triangle Meshes}},
author = {
Ezuz, Danielle
and
Heeren, Behrend
and
Azencot, Omri
and
Rumpf, Martin
and
Ben-Chen, Mirela
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13624}
}
                
@article{
10.1111:cgf.13625,
journal = {Computer Graphics Forum}, title = {{
Exact Constraint Satisfaction for Truly Seamless Parametrization}},
author = {
Mandad, Manish
and
Campen, Marcel
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13625}
}
                
@article{
10.1111:cgf.13626,
journal = {Computer Graphics Forum}, title = {{
A Low-Dimensional Function Space for Efficient Spectral Upsampling}},
author = {
Jakob, Wenzel
and
Hanika, Johannes
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13626}
}
                
@article{
10.1111:cgf.13627,
journal = {Computer Graphics Forum}, title = {{
Accurate Synthesis of Multi-Class Disk Distributions}},
author = {
Ecormier-Nocca, Pierre
and
Memari, Pooran
and
Gain, James
and
Cani, Marie-Paule
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13627}
}
                
@article{
10.1111:cgf.13628,
journal = {Computer Graphics Forum}, title = {{
Learning to Importance Sample in Primary Sample Space}},
author = {
Zheng, Quan
and
Zwicker, Matthias
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13628}
}
                
@article{
10.1111:cgf.13629,
journal = {Computer Graphics Forum}, title = {{
Character Navigation in Dynamic Environments Based on Optical Flow}},
author = {
López, Axel
and
Francois, Chaumette
and
Marchand, Eric
and
Pettré, Julien
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13629}
}
                
@article{
10.1111:cgf.13630,
journal = {Computer Graphics Forum}, title = {{
Deep HDR Video from Sequences with Alternating Exposures}},
author = {
Kalantari, Nima Khademi
and
Ramamoorthi, Ravi
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13630}
}
                
@article{
10.1111:cgf.13631,
journal = {Computer Graphics Forum}, title = {{
Clear Skies Ahead: Towards Real-Time Automatic Sky Replacement in Video}},
author = {
Halperin, Tavi
and
Cain, Harel
and
Bibi, Ofir
and
Werman, Michael
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13631}
}
                
@article{
10.1111:cgf.13632,
journal = {Computer Graphics Forum}, title = {{
Deep Video-Based Performance Cloning}},
author = {
Aberman, Kfir
and
Shi, Mingyi
and
Liao, Jing
and
Lischinski, Dani
and
Chen, Baoquan
and
Cohen-Or, Daniel
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13632}
}
                
@article{
10.1111:cgf.13633,
journal = {Computer Graphics Forum}, title = {{
Neural BTF Compression and Interpolation}},
author = {
Rainer, Gilles
and
Jakob, Wenzel
and
Ghosh, Abhijeet
and
Weyrich, Tim
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13633}
}
                
@article{
10.1111:cgf.13634,
journal = {Computer Graphics Forum}, title = {{
Gradient Outlier Removal for Gradient-Domain Path Tracing}},
author = {
Ha, Saerom
and
Oh, Sojin
and
Back, Jonghee
and
Yoon, Sung-Eui
and
Moon, Bochang
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13634}
}
                
@article{
10.1111:cgf.13635,
journal = {Computer Graphics Forum}, title = {{
Generating Stochastic Wall Patterns On-the-fly with Wang Tiles}},
author = {
Derouet-Jourdan, Alexandre
and
Salvati, Marc
and
Jonchier, Théo
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13635}
}
                
@article{
10.1111:cgf.13636,
journal = {Computer Graphics Forum}, title = {{
Generating Color Scribble Images using Multi-layered Monochromatic Strokes Dithering}},
author = {
Lo, Yi-Hsiang
and
Lee, Ruen-Rone
and
Chu, Hung-Kuo
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13636}
}
                
@article{
10.1111:cgf.13637,
journal = {Computer Graphics Forum}, title = {{
Multi-Pose Interactive Linkage Design}},
author = {
Nishida, Gen
and
Bousseau, Adrien
and
Aliaga, Daniel
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13637}
}
                
@article{
10.1111:cgf.13638,
journal = {Computer Graphics Forum}, title = {{
Computational Design of Steady 3D Dissection Puzzles}},
author = {
Tang, Keke
and
Song, Peng
and
Wang, Xiaofei
and
Deng, Bailin
and
Fu, Chi-Wing
and
Liu, Ligang
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13638}
}
                
@article{
10.1111:cgf.13639,
journal = {Computer Graphics Forum}, title = {{
Object Partitioning for Support-Free 3D-Printing}},
author = {
Karasik, Eli
and
Fattal, Raanan
and
Werman, Michael
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13639}
}
                
@article{
10.1111:cgf.13641,
journal = {Computer Graphics Forum}, title = {{
Geometry Aware Tori Decomposition}},
author = {
Chen, Jia
and
Gopi, Meenakshisundaram
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13641}
}
                
@article{
10.1111:cgf.13640,
journal = {Computer Graphics Forum}, title = {{
Dynamic Visibility-Driven Molecular Surfaces}},
author = {
Bruckner, Stefan
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13640}
}
                
@article{
10.1111:cgf.13642,
journal = {Computer Graphics Forum}, title = {{
Design and Automated Generation of Japanese Picture Puzzles}},
author = {
Kerkhof, Mees van de
and
Jong, Tim de
and
Parment, Raphael
and
Löffler, Maarten
and
Vaxman, Amir
and
van Kreveld, Marc
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13642}
}
                
@article{
10.1111:cgf.13643,
journal = {Computer Graphics Forum}, title = {{
Learning-Based Animation of Clothing for Virtual Try-On}},
author = {
Santesteban, Igor
and
Otaduy, Miguel A.
and
Casas, Dan
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13643}
}
                
@article{
10.1111:cgf.13644,
journal = {Computer Graphics Forum}, title = {{
Learning a Generative Model for Multi-Step Human-Object Interactions from Videos}},
author = {
Wang, He
and
Pirk, Sören
and
Yumer, Ersin
and
Kim, Vladimir
and
Sener, Ozan
and
Sridhar, Srinath
and
Guibas, Leonidas
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13644}
}
                
@article{
10.1111:cgf.13645,
journal = {Computer Graphics Forum}, title = {{
Latent-space Dynamics for Reduced Deformable Simulation}},
author = {
Fulton, Lawson
and
Modi, Vismay
and
Duvenaud, David
and
Levin, David I. W.
and
Jacobson, Alec
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13645}
}
                
@article{
10.1111:cgf.13646,
journal = {Computer Graphics Forum}, title = {{
Controlling Motion Blur in Synthetic Long Time Exposures}},
author = {
Lancelle, Marcel
and
Dogan, Pelin
and
Gross, Markus
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13646}
}
                
@article{
10.1111:cgf.13647,
journal = {Computer Graphics Forum}, title = {{
What's in a Face? Metric Learning for Face Characterization}},
author = {
Sendik, Omry
and
Lischinski, Dani
and
Cohen-Or, Daniel
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13647}
}
                
@article{
10.1111:cgf.13648,
journal = {Computer Graphics Forum}, title = {{
Exploratory Stage Lighting Design using Visual Objectives}},
author = {
Shimizu, Evan
and
Paris, Sylvain
and
Fisher, Matthew
and
Yumer, Ersin
and
Fatahalian, Kayvon
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13648}
}
                
@article{
10.1111:cgf.13649,
journal = {Computer Graphics Forum}, title = {{
A CNN-based Flow Correction Method for Fast Preview}},
author = {
Xiao, Xiangyun
and
Wang, Hui
and
Yang, Xubo
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13649}
}
                
@article{
10.1111:cgf.13650,
journal = {Computer Graphics Forum}, title = {{
Practical Person-Specific Eye Rigging}},
author = {
Bérard, Pascal
and
Bradley, Derek
and
Gross, Markus
and
Beeler, Thabo
}, year = {
2019},
publisher = {
The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {
10.1111/cgf.13650}
}

Browse

Recent Submissions

Now showing 1 - 38 of 38
  • Item
    EUROGRAPHICS 2019: CGF 38-2 Frontmatter
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Alliez, Pierre; Pellacini, Fabio; Alliez, Pierre and Pellacini, Fabio
    -
  • Item
    Procedural Tectonic Planets
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Cortial, Yann; Peytavie, Adrien; Galin, Eric; Guérin, Eric; Alliez, Pierre and Pellacini, Fabio
    We present a procedural method for authoring synthetic tectonic planets. Instead of relying on computationally demanding physically-based simulations, we capture the fundamental phenomena into a procedural method that faithfully reproduces largescale planetary features generated by the movement and collision of the tectonic plates. We approximate complex phenomena such as plate subduction or collisions to deform the lithosphere, including the continental and oceanic crusts. The user can control the movement of the plates, which dynamically evolve and generate a variety of landforms such as continents, oceanic ridges, large scale mountain ranges or island arcs. Finally, we amplify the large-scale planet model with either procedurallydefined or real-world elevation data to synthesize coherent detailed reliefs. Our method allows the user to control the evolution of an entire planet interactively, and to trigger specific events such as catastrophic plate rifting.
  • Item
    Local Editing of Procedural Models
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Lipp, Markus; Specht, Matthias; Lau, Cheryl; Wonka, Peter; Mueller, Pascal; Alliez, Pierre and Pellacini, Fabio
    Procedural modeling is used across many industries for rapid 3D content creation. However, professional procedural tools often lack artistic control, requiring manual edits on baked results, diminishing the advantages of a procedural modeling pipeline. Previous approaches to enable local artistic control require special annotations of the procedural system and manual exploration of potential edit locations. Therefore, we propose a novel approach to discover meaningful and non-redundant good edit locations (GELs). We introduce a bottom-up algorithm for finding GELs directly from the attributes in procedural models, without special annotations. To make attribute edits at GELs persistent, we analyze their local spatial context and construct a meta-locator to uniquely specify their structure. Meta-locators are calculated independently per attribute, making them robust against changes in the procedural system. Functions on meta-locators enable intuitive and robust multi-selections. Finally, we introduce an algorithm to transfer meta-locators to a different procedural model. We show that our approach greatly simplifies the exploration of the local edit space, and we demonstrate its usefulness in a user study and multiple real-world examples.
  • Item
    String-Based Synthesis of Structured Shapes
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Kalojanov, Javor; Lim, Isaak; Mitra, Niloy; Kobbelt, Leif; Alliez, Pierre and Pellacini, Fabio
    We propose a novel method to synthesize geometric models from a given class of context-aware structured shapes such as buildings and other man-made objects. The central idea is to leverage powerful machine learning methods from the area of natural language processing for this task. To this end, we propose a technique that maps shapes to strings and vice versa, through an intermediate shape graph representation. We then convert procedurally generated shape repositories into text databases that, in turn, can be used to train a variational autoencoder. The autoencoder enables higher level shape manipulation and synthesis like, for example, interpolation and sampling via its continuous latent space. We provide project code and pre-trained models.
  • Item
    Dual Sheet Meshing: An Interactive Approach to Robust Hexahedralization
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Takayama, Kenshi; Alliez, Pierre and Pellacini, Fabio
    The combinatorial dual of a hex mesh induces a collection of mutually intersecting surfaces (dual sheets). Inspired by Campen et al.'s work on quad meshing [CBK12,CK14], we propose to directly generate such dual sheets so that, as long as the volume is properly partitioned by the dual sheets, we are guaranteed to arrive at a valid all-hex mesh topology. Since automatically generating dual sheets seems much harder than the 2D counterpart, we chose to leave the task to the user; our system is equipped with a few simple 3D modeling tools for interactively designing dual sheets. Dual sheets are represented as implicit surfaces in our approach, greatly simplifying many of the computational steps such as finding intersections and analyzing topology. We also propose a simple algorithm for primalizing the dual graph where each dual cell, often enclosing singular edges, gets mapped onto a reference polyhedron via harmonic parameterization. Preservation of sharp features is simply achieved by modifying the boundary conditions. We demonstrate the feasibility of our approach through various modeling examples.
  • Item
    A Geometrically Consistent Viscous Fluid Solver with Two-Way Fluid-Solid Coupling
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Takahashi, Tetsuya; Lin, Ming C.; Alliez, Pierre and Pellacini, Fabio
    We present a grid-based fluid solver for simulating viscous materials and their interactions with solid objects. Our method formulates the implicit viscosity integration as a minimization problem with consistently estimated volume fractions to account for the sub-grid details of free surfaces and solid boundaries. To handle the interplay between fluids and solid objects with viscosity forces, we also formulate the two-way fluid-solid coupling as a unified minimization problem based on the variational principle, which naturally enforces the boundary conditions. Our formulation leads to a symmetric positive definite linear system with a sparse matrix regardless of the monolithically coupled solid objects. Additionally, we present a position-correction method using density constraints to enforce the uniform distributions of fluid particles and thus prevent the loss of fluid volumes. We demonstrate the effectiveness of our method in a wide range of viscous fluid scenarios.
  • Item
    Deep Fluids: A Generative Network for Parameterized Fluid Simulations
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Kim, Byungsoo; Azevedo, Vinicius C.; Thuerey, Nils; Kim, Theodore; Gross, Markus; Solenthaler, Barbara; Alliez, Pierre and Pellacini, Fabio
    This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters. A convolutional neural network is trained on a collection of discrete, parameterizable fluid simulation velocity fields. Due to the capability of deep learning architectures to learn representative features of the data, our generative model is able to accurately approximate the training data set, while providing plausible interpolated in-betweens. The proposed generative model is optimized for fluids by a novel loss function that guarantees divergence-free velocity fields at all times. In addition, we demonstrate that we can handle complex parameterizations in reduced spaces, and advance simulations in time by integrating in the latent space with a second network. Our method models a wide variety of fluid behaviors, thus enabling applications such as fast construction of simulations, interpolation of fluids with different parameters, time re-sampling, latent space simulations, and compression of fluid simulation data. Reconstructed velocity fields are generated up to 700x faster than re-simulating the data with the underlying CPU solver, while achieving compression rates of up to 1300x.
  • Item
    Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Wiewel, Steffen; Becher, Moritz; Thuerey, Nils; Alliez, Pierre and Pellacini, Fabio
    We propose a method for the data-driven inference of temporal evolutions of physical functions with deep learning. More specifically, we target fluid flow problems, and we propose a novel LSTM-based approach to predict the changes of the pressure field over time. The central challenge in this context is the high dimensionality of Eulerian space-time data sets. We demonstrate for the first time that dense 3D+time functions of physics system can be predicted within the latent spaces of neural networks, and we arrive at a neural-network based simulation algorithm with significant practical speed-ups. We highlight the capabilities of our method with a series of complex liquid simulations, and with a set of single-phase buoyancy simulations. With a set of trained networks, our method is more than two orders of magnitudes faster than a traditional pressure solver. Additionally, we present and discuss a series of detailed evaluations for the different components of our algorithm.
  • Item
    StyleBlit: Fast Example-Based Stylization with Local Guidance
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Sýkora, Daniel; Jamriška, Ondrej; Texler, Ondrej; Fišer, Jakub; Lukác, Mike; Lu, Jingwan; Shechtman, Eli; Alliez, Pierre and Pellacini, Fabio
    We present StyleBlit-an efficient example-based style transfer algorithm that can deliver high-quality stylized renderings in real-time on a single-core CPU. Our technique is especially suitable for style transfer applications that use local guidance - descriptive guiding channels containing large spatial variations. Local guidance encourages transfer of content from the source exemplar to the target image in a semantically meaningful way. Typical local guidance includes, e.g., normal values, texture coordinates or a displacement field. Contrary to previous style transfer techniques, our approach does not involve any computationally expensive optimization. We demonstrate that when local guidance is used, optimization-based techniques converge to solutions that can be well approximated by simple pixel-level operations. Inspired by this observation, we designed an algorithm that produces results visually similar to, if not better than, the state-of-the-art, and is several orders of magnitude faster. Our approach is suitable for scenarios with low computational budget such as games and mobile applications.
  • Item
    Hierarchical Rasterization of Curved Primitives for Vector Graphics Rendering on the GPU
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Dokter, Mark; Hladký, Jozef; Parger, Mathias; Schmalstieg, Dieter; Seidel, Hans-Peter; Steinberger, Markus; Alliez, Pierre and Pellacini, Fabio
    In this paper, we introduce the CPatch, a curved primitive that can be used to construct arbitrary vector graphics. A CPatch is a generalization of a 2D polygon: Any number of curves up to a cubic degree bound a primitive. We show that a CPatch can be rasterized efficiently in a hierarchical manner on the GPU, locally discarding irrelevant portions of the curves. Our rasterizer is fast and scalable, works on all patches in parallel, and does not require any approximations. We show a parallel implementation of our rasterizer, which naturally supports all kinds of color spaces, blending and super-sampling. Additionally, we show how vector graphics input can efficiently be converted to a CPatch representation, solving challenges like patch self-intersections and false inside-outside classification. Results indicate that our approach is faster than the state-of-the-art, more flexible and could potentially be implemented in hardware.
  • Item
    A Subspace Method for Fast Locally Injective Harmonic Mapping
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Hefetz, Eden Fedida; Chien, Edward; Weber, Ofir; Alliez, Pierre and Pellacini, Fabio
    We present a fast algorithm for low-distortion locally injective harmonic mappings of genus 0 triangle meshes with and without cone singularities. The algorithm consists of two portions, a linear subspace analysis and construction, and a nonlinear nonconvex optimization for determination of a mapping within the reduced subspace. The subspace is the space of solutions to the Harmonic Global Parametrization (HGP) linear system [BCW17], and only vertex positions near cones are utilized, decoupling the variable count from the mesh density. A key insight shows how to construct the linear subspace at a cost comparable to that of a linear solve, extracting a very small set of elements from the inverse of the matrix without explicitly calculating it. With a variable count on the order of the number of cones, a tangential alternating projection method [HCW17] and a subsequent Newton optimization [CW17] are used to quickly find a low-distortion locally injective mapping. This mapping determination is typically much faster than the subspace construction. Experiments demonstrating its speed and efficacy are shown, and we find it to be an order of magnitude faster than HGP and other alternatives.
  • Item
    Elastic Correspondence between Triangle Meshes
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Ezuz, Danielle; Heeren, Behrend; Azencot, Omri; Rumpf, Martin; Ben-Chen, Mirela; Alliez, Pierre and Pellacini, Fabio
    We propose a novel approach for shape matching between triangular meshes that, in contrast to existing methods, can match crease features. Our approach is based on a hybrid optimization scheme, that solves simultaneously for an elastic deformation of the source and its projection on the target. The elastic energy we minimize is invariant to rigid body motions, and its non-linear membrane energy component favors locally injective maps. Symmetrizing this model enables feature aligned correspondences even for non-isometric meshes. We demonstrate the advantage of our approach over state of the art methods on isometric and non-isometric datasets, where we improve the geodesic distance from the ground truth, the conformal and area distortions, and the mismatch of the mean curvature functions. Finally, we show that our computed maps are applicable for surface interpolation, consistent cross-field computation, and consistent quadrangular remeshing of a set of shapes.
  • Item
    Exact Constraint Satisfaction for Truly Seamless Parametrization
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Mandad, Manish; Campen, Marcel; Alliez, Pierre and Pellacini, Fabio
    In the field of global surface parametrization a recent focus has been on so-called seamless parametrization. This term refers to parametrization approaches which, while using an atlas of charts to enable the handling of surfaces of arbitrary topology, relate the parametrization across the cuts between charts via transition functions from special classes of transformations. This effectively makes the cuts invisible to applications which are invariant to these specific transformations in some sense. In actual implementations of these parametrization approaches, however, these restrictions are obeyed only approximately; errors stem from the tolerances of numerical solvers employed and, ultimately, from the limited accuracy of floating point arithmetic. In practice, robustness issues arise from these flaws in the seamlessness of a parametrization, no matter how small. We present a robust global algorithm that turns a given approximately seamless parametrization into an exactly seamless one - that still is representable by standard floating point numbers. It supports common practically relevant additional constraints regarding boundary and feature curve alignment or isocurve connectivity, and ensures that these are likewise fulfilled exactly. This allows subsequent algorithms to operate robustly on the resulting truly seamless parametrization. We believe that the core of our method will furthermore be of benefit in a broader range of applications involving linearly constrained numerical optimization.
  • Item
    A Low-Dimensional Function Space for Efficient Spectral Upsampling
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Jakob, Wenzel; Hanika, Johannes; Alliez, Pierre and Pellacini, Fabio
    We present a versatile technique to convert textures with tristimulus colors into the spectral domain, allowing such content to be used in modern rendering systems. Our method is based on the observation that suitable reflectance spectra can be represented using a low-dimensional parametric model that is intrinsically smooth and energy-conserving, which leads to significant simplifications compared to prior work. The resulting spectral textures are compact and efficient: storage requirements are identical to standard RGB textures, and as few as six floating point instructions are required to evaluate them at any wavelength. Our model is the first spectral upsampling method to achieve zero error on the full sRGB gamut. The technique also supports large-gamut color spaces, and can be vectorized effectively for use in rendering systems that handle many wavelengths at once.
  • Item
    Accurate Synthesis of Multi-Class Disk Distributions
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Ecormier-Nocca, Pierre; Memari, Pooran; Gain, James; Cani, Marie-Paule; Alliez, Pierre and Pellacini, Fabio
    While analysing and synthesising 2D distributions of points has been applied both to the generation of textures with discrete elements and for populating virtual worlds with 3D objects, the results are often inaccurate since the spatial extent of objects cannot be expressed.We introduce three improvements enabling the synthesis of more general distributions of elements. First, we extend continuous pair correlation function (PCF) algorithms to multi-class distributions using a dependency graph, thereby capturing interrelationships between distinct categories of objects. Second, we introduce a new normalised metric for disks, which makes the method applicable to both point and possibly overlapping disk distributions. The metric is specifically designed to distinguish perceptually salient features, such as disjoint, tangent, overlapping, or nested disks. Finally, we pay particular attention to convergence of the mean PCF as well as the validity of individual PCFs, by taking into consideration the variance of the input. Our results demonstrate that this framework can capture and reproduce real-life distributions of elements representing a variety of complex semi-structured patterns, from the interaction between trees and the understorey in a forest to droplets of water. More generally, it applies to any category of 2D object whose shape is better represented by bounding circles than points.
  • Item
    Learning to Importance Sample in Primary Sample Space
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Zheng, Quan; Zwicker, Matthias; Alliez, Pierre and Pellacini, Fabio
    Importance sampling is one of the most widely used variance reduction strategies in Monte Carlo rendering. We propose a novel importance sampling technique that uses a neural network to learn how to sample from a desired density represented by a set of samples. Our approach considers an existing Monte Carlo rendering algorithm as a black box. During a scene-dependent training phase, we learn to generate samples with a desired density in the primary sample space of the renderer using maximum likelihood estimation. We leverage a recent neural network architecture that was designed to represent real-valued non-volume preserving (''Real NVP'') transformations in high dimensional spaces. We use Real NVP to non-linearly warp primary sample space and obtain desired densities. In addition, Real NVP efficiently computes the determinant of the Jacobian of the warp, which is required to implement the change of integration variables implied by the warp. A main advantage of our approach is that it is agnostic of underlying light transport effects, and can be combined with an existing rendering technique by treating it as a black box. We show that our approach leads to effective variance reduction in several practical scenarios.
  • Item
    Character Navigation in Dynamic Environments Based on Optical Flow
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) López, Axel; Francois, Chaumette; Marchand, Eric; Pettré, Julien; Alliez, Pierre and Pellacini, Fabio
    Steering and navigation are important components of character animation systems to enable them to autonomously move in their environment. In this work, we propose a synthetic vision model that uses visual features to steer agents through dynamic environments. Our agents perceive optical flow resulting from their relative motion with the objects of the environment. The optical flow is then segmented and processed to extract visual features such as the focus of expansion and time-to-collision. Then, we establish the relations between these visual features and the agent motion, and use them to design a set of control functions which allow characters to perform object-dependent tasks, such as following, avoiding and reaching. Control functions are then combined to let characters perform more complex navigation tasks in dynamic environments, such as reaching a goal while avoiding multiple obstacles. Agent's motion is achieved by local minimization of these functions. We demonstrate the efficiency of our approach through a number of scenarios. Our work sets the basis for building a character animation system which imitates human sensorimotor actions. It opens new perspectives to achieve realistic simulation of human characters taking into account perceptual factors, such as the lighting conditions of the environment.
  • Item
    Deep HDR Video from Sequences with Alternating Exposures
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Kalantari, Nima Khademi; Ramamoorthi, Ravi; Alliez, Pierre and Pellacini, Fabio
    A practical way to generate a high dynamic range (HDR) video using off-the-shelf cameras is to capture a sequence with alternating exposures and reconstruct the missing content at each frame. Unfortunately, existing approaches are typically slow and are not able to handle challenging cases. In this paper, we propose a learning-based approach to address this difficult problem. To do this, we use two sequential convolutional neural networks (CNN) to model the entire HDR video reconstruction process. In the first step, we align the neighboring frames to the current frame by estimating the flows between them using a network, which is specifically designed for this application. We then combine the aligned and current images using another CNN to produce the final HDR frame. We perform an end-to-end training by minimizing the error between the reconstructed and ground truth HDR images on a set of training scenes. We produce our training data synthetically from existing HDR video datasets and simulate the imperfections of standard digital cameras using a simple approach. Experimental results demonstrate that our approach produces high-quality HDR videos and is an order of magnitude faster than the state-of-the-art techniques for sequences with two and three alternating exposures.
  • Item
    Clear Skies Ahead: Towards Real-Time Automatic Sky Replacement in Video
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Halperin, Tavi; Cain, Harel; Bibi, Ofir; Werman, Michael; Alliez, Pierre and Pellacini, Fabio
    Digital videos such as those captured by a smartphone often exhibit exposure inconsistencies, a poorly exposed sky, or simply suffer from an uninteresting or plain looking sky. Professionals may edit these videos using advanced and time-consuming tools unavailable to most users, to replace the sky with a more expressive or imaginative sky. In this work, we propose an algorithm for automatic replacement of the sky region in a video with a different sky, providing nonprofessional users with a simple yet efficient tool to seamlessly replace the sky. The method is fast, achieving close to real-time performance on mobile devices and the user's involvement can remain as limited as simply selecting the replacement sky.
  • Item
    Deep Video-Based Performance Cloning
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Aberman, Kfir; Shi, Mingyi; Liao, Jing; Lischinski, Dani; Chen, Baoquan; Cohen-Or, Daniel; Alliez, Pierre and Pellacini, Fabio
    We present a new video-based performance cloning technique. After training a deep generative network using a reference video capturing the appearance and dynamics of a target actor, we are able to generate videos where this actor reenacts other performances. All of the training data and the driving performances are provided as ordinary video segments, without motion capture or depth information. Our generative model is realized as a deep neural network with two branches, both of which train the same space-time conditional generator, using shared weights. One branch, responsible for learning to generate the appearance of the target actor in various poses, uses paired training data, self-generated from the reference video. The second branch uses unpaired data to improve generation of temporally coherent video renditions of unseen pose sequences. Through data augmentation, our network is able to synthesize images of the target actor in poses never captured by the reference video. We demonstrate a variety of promising results, where our method is able to generate temporally coherent videos, for challenging scenarios where the reference and driving videos consist of very different dance performances.
  • Item
    Neural BTF Compression and Interpolation
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Rainer, Gilles; Jakob, Wenzel; Ghosh, Abhijeet; Weyrich, Tim; Alliez, Pierre and Pellacini, Fabio
    The Bidirectional Texture Function (BTF) is a data-driven solution to render materials with complex appearance. A typical capture contains tens of thousands of images of a material sample under varying viewing and lighting conditions.While capable of faithfully recording complex light interactions in the material, the main drawback is the massive memory requirement, both for storing and rendering, making effective compression of BTF data a critical component in practical applications. Common compression schemes used in practice are based on matrix factorization techniques, which preserve the discrete format of the original dataset. While this approach generalizes well to different materials, rendering with the compressed dataset still relies on interpolating between the closest samples. Depending on the material and the angular resolution of the BTF, this can lead to blurring and ghosting artefacts. An alternative approach uses analytic model fitting to approximate the BTF data, using continuous functions that naturally interpolate well, but whose expressive range is often not wide enough to faithfully recreate materials with complex non-local lighting effects (subsurface scattering, inter-reflections, shadowing and masking...). In light of these observations, we propose a neural network-based BTF representation inspired by autoencoders: our encoder compresses each texel to a small set of latent coefficients, while our decoder additionally takes in a light and view direction and outputs a single RGB vector at a time. This allows us to continuously query reflectance values in the light and view hemispheres, eliminating the need for linear interpolation between discrete samples. We train our architecture on fabric BTFs with a challenging appearance and compare to standard PCA as a baseline. We achieve competitive compression ratios and high-quality interpolation/extrapolation without blurring or ghosting artifacts.
  • Item
    Gradient Outlier Removal for Gradient-Domain Path Tracing
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Ha, Saerom; Oh, Sojin; Back, Jonghee; Yoon, Sung-Eui; Moon, Bochang; Alliez, Pierre and Pellacini, Fabio
    We present a new outlier removal technique for a gradient-domain path tracing (G-PT) that computes image gradients as well as colors. Our approach rejects gradient outliers whose estimated errors are much higher than those of the other gradients for improving reconstruction quality for the G-PT. We formulate our outlier removal problem as a least trimmed squares optimization, which employs only a subset of gradients so that a final image can be reconstructed without including the gradient outliers. In addition, we design this outlier removal process so that the chosen subset of gradients maintains connectivity through gradients between pixels, preventing pixels from being isolated. Lastly, the optimal number of inlier gradients is estimated to minimize our reconstruction error. We have demonstrated that our reconstruction with robustly rejecting gradient outliers produces visually and numerically improved results, compared to the previous screened Poisson reconstruction that uses all the gradients.
  • Item
    Generating Stochastic Wall Patterns On-the-fly with Wang Tiles
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Derouet-Jourdan, Alexandre; Salvati, Marc; Jonchier, Théo; Alliez, Pierre and Pellacini, Fabio
    The game and movie industries always face the challenge of reproducing materials. This problem is tackled by combining illumination models and various textures (painted or procedural patterns). Generating stochastic wall patterns is crucial in the creation of a wide range of backgrounds (castles, temples, ruins...). A specific Wang tile set was introduced previously to tackle this problem, in an iterative fashion. However, long lines may appear as visual artifacts. We use this tile set in a new on-the-fly procedure to generate stochastic wall patterns. For this purpose, we introduce specific hash functions implementing a constrained Wang tiling. This technique makes possible the generation of boundless textures while giving control over the maximum line length. The algorithm is simple and easy to implement, and the wall structure we get from the tiles allows to achieve visuals that reproduce all the small details of artist painted walls.
  • Item
    Generating Color Scribble Images using Multi-layered Monochromatic Strokes Dithering
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Lo, Yi-Hsiang; Lee, Ruen-Rone; Chu, Hung-Kuo; Alliez, Pierre and Pellacini, Fabio
    Color scribbling is a unique form of illustration where artists use compact, overlapping, and monochromatic scribbles at microscopic scale to create astonishing colorful images at macroscopic scale. The creation process is skill-demanded and time-consuming, which typically involves drawing monochromatic scribbles layer-by-layer to depict true-color subjects using a limited color palette delicately. In this work, we present a novel computational framework for automatic generation of color scribble images from arbitrary raster images. The core contribution of our work lies in a novel color dithering model tailormade for synthesizing a smooth color appearance using multiple layers of overlapped monochromatic strokes. Specifically, our system reconstructs the appearance of the input image by (i) generating layers of monochromatic scribbles based on a limited color palette derived from input image, and (ii) optimizing the drawing sequence among layers to minimize the visual color dissimilarity between dithered image and original image as well as the color banding artifacts. We demonstrate the effectiveness and robustness of our algorithm with various convincing results synthesized from a variety of input images with different stroke patterns. The experimental study further shows that our approach faithfully captures the scribble style and the color presentation at respectively microscopic and macroscopic scales, which is otherwise difficult for state-of-the-art methods.
  • Item
    Multi-Pose Interactive Linkage Design
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Nishida, Gen; Bousseau, Adrien; Aliaga, Daniel; Alliez, Pierre and Pellacini, Fabio
    We introduce an interactive tool for novice users to design mechanical objects made of 2.5D linkages. Users simply draw the shape of the object and a few key poses of its multiple moving parts. Our approach automatically generates a one-degree-offreedom linkage that connects the fixed and moving parts, such that the moving parts traverse all input poses in order without any collision with the fixed and other moving parts. In addition, our approach avoids common linkage defects and favors compact linkages and smooth motion trajectories. Finally, our system automatically generates the 3D geometry of the object and its links, allowing the rapid creation of a physical mockup of the designed object.
  • Item
    Computational Design of Steady 3D Dissection Puzzles
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Tang, Keke; Song, Peng; Wang, Xiaofei; Deng, Bailin; Fu, Chi-Wing; Liu, Ligang; Alliez, Pierre and Pellacini, Fabio
    Dissection puzzles require assembling a common set of pieces into multiple distinct forms. Existing works focus on creating 2D dissection puzzles that form primitive or naturalistic shapes. Unlike 2D dissection puzzles that could be supported on a tabletop surface, 3D dissection puzzles are preferable to be steady by themselves for each assembly form. In this work, we aim at computationally designing steady 3D dissection puzzles. We address this challenging problem with three key contributions. First, we take two voxelized shapes as inputs and dissect them into a common set of puzzle pieces, during which we allow slightly modifying the input shapes, preferably on their internal volume, to preserve the external appearance. Second, we formulate a formal model of generalized interlocking for connecting pieces into a steady assembly using both their geometric arrangements and friction. Third, we modify the geometry of each dissected puzzle piece based on the formal model such that each assembly form is steady accordingly. We demonstrate the effectiveness of our approach on a wide variety of shapes, compare it with the state-of-the-art on 2D and 3D examples, and fabricate some of our designed puzzles to validate their steadiness.
  • Item
    Object Partitioning for Support-Free 3D-Printing
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Karasik, Eli; Fattal, Raanan; Werman, Michael; Alliez, Pierre and Pellacini, Fabio
    Fused deposition modeling based 3D-printing is becoming increasingly popular due to it's low-cost and simple operation and maintenance. While it produces rugged prints made from a wide range of materials, it suffers from an inherent printing limitation where it cannot produce overhanging surfaces of non-trivial size. This limitation can be handled by constructing temporary support-structures, however this solution involves additional material costs, longer print time, and often a fair amount of labor in removing it. In this paper we present a new method for partitioning general solid objects into a small number of parts that can be printed with no support. The partitioning is computed by applying a sequence of cutting-planes that split the object recursively. Unlike existing algorithms, the planes are not chosen at random, rather they are derived from shape analysis routines that identify and resolve various commonly-found geometric configurations. In addition, we guide this search by a revised set of conditions that both ensure the objects' printability as well as realistically model the printing capabilities of the printer at hand. Evaluation of the new method demonstrates its ability to efficiently obtain support-free partitionings typically containing fewer parts compared to existing methods that rely on support-structures.
  • Item
    Geometry Aware Tori Decomposition
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Chen, Jia; Gopi, Meenakshisundaram; Alliez, Pierre and Pellacini, Fabio
    This work presents a shape decomposition algorithm to partition a complex high genus surface into simple primitives, each of which is a torus. First, using a novel iterative algorithm, handle and tunnel fundamental cycles on the surface are progressively localized. Then, the problem of computing the splitting cycles that produce such a tori decomposition is posed as a min-cut problem on the mesh's dual graph with earlier computed tunnels as source and target. The edge weights for the min-cut problem are designed for the cut to be geometry-aware. We present an implementation and demonstrate the results of our algorithm on numerous examples.
  • Item
    Dynamic Visibility-Driven Molecular Surfaces
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Bruckner, Stefan; Alliez, Pierre and Pellacini, Fabio
    Molecular surface representations are an important tool for the visual analysis of molecular structure and function. In this paper, we present a novel method for the visualization of dynamic molecular surfaces based on the Gaussian model. In contrast to previous approaches, our technique does not rely on the construction of intermediate representations such as grids or triangulated surfaces. Instead, it operates entirely in image space, which enables us to exploit visibility information to efficiently skip unnecessary computations. With this visibility-driven approach, we can visualize dynamic high-quality surfaces for molecules consisting of millions of atoms. Our approach requires no preprocessing, allows for the interactive adjustment of all properties and parameters, and is significantly faster than previous approaches, while providing superior quality.
  • Item
    Design and Automated Generation of Japanese Picture Puzzles
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Kerkhof, Mees van de; Jong, Tim de; Parment, Raphael; Löffler, Maarten; Vaxman, Amir; van Kreveld, Marc; Alliez, Pierre and Pellacini, Fabio
    We introduce the generalized nonogram, an extension of the well-known nonogram or Japanese picture puzzle. It is not based on a regular square grid but on a subdivision (arrangement) with differently shaped cells, bounded by straight lines or curves. To generate a good, clear puzzle from a filled line drawing, the arrangement that is formed for the puzzle must meet a number of criteria. Some of these relate to the puzzle and some to the geometry. We give an overview of these criteria and show that a puzzle can be generated by an optimization method like simulated annealing. Experimentally, we analyze the convergence of the method and the remaining penalty score on several input pictures along with various other design options.
  • Item
    Learning-Based Animation of Clothing for Virtual Try-On
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Santesteban, Igor; Otaduy, Miguel A.; Casas, Dan; Alliez, Pierre and Pellacini, Fabio
    This paper presents a learning-based clothing animation method for highly efficient virtual try-on simulation. Given a garment, we preprocess a rich database of physically-based dressed character simulations, for multiple body shapes and animations. Then, using this database, we train a learning-based model of cloth drape and wrinkles, as a function of body shape and dynamics. We propose a model that separates global garment fit, due to body shape, from local garment wrinkles, due to both pose dynamics and body shape. We use a recurrent neural network to regress garment wrinkles, and we achieve highly plausible nonlinear effects, in contrast to the blending artifacts suffered by previous methods. At runtime, dynamic virtual try-on animations are produced in just a few milliseconds for garments with thousands of triangles. We show qualitative and quantitative analysis of results.
  • Item
    Learning a Generative Model for Multi-Step Human-Object Interactions from Videos
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Wang, He; Pirk, Sören; Yumer, Ersin; Kim, Vladimir; Sener, Ozan; Sridhar, Srinath; Guibas, Leonidas; Alliez, Pierre and Pellacini, Fabio
    Creating dynamic virtual environments consisting of humans interacting with objects is a fundamental problem in computer graphics. While it is well-accepted that agent interactions play an essential role in synthesizing such scenes, most extant techniques exclusively focus on static scenes, leaving the dynamic component out. In this paper, we present a generative model to synthesize plausible multi-step dynamic human-object interactions. Generating multi-step interactions is challenging since the space of such interactions is exponential in the number of objects, activities, and time steps. We propose to handle this combinatorial complexity by learning a lower dimensional space of plausible human-object interactions. We use action plots to represent interactions as a sequence of discrete actions along with the participating objects and their states. To build action plots, we present an automatic method that uses state-of-the-art computer vision techniques on RGB videos in order to detect individual objects and their states, extract the involved hands, and recognize the actions performed. The action plots are built from observing videos of everyday activities and are used to train a generative model based on a Recurrent Neural Network (RNN). The network learns the causal dependencies and constraints between individual actions and can be used to generate novel and diverse multi-step human-object interactions. Our representation and generative model allows new capabilities in a variety of applications such as interaction prediction, animation synthesis, and motion planning for a real robotic agent.
  • Item
    Latent-space Dynamics for Reduced Deformable Simulation
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Fulton, Lawson; Modi, Vismay; Duvenaud, David; Levin, David I. W.; Jacobson, Alec; Alliez, Pierre and Pellacini, Fabio
    We propose the first reduced model simulation framework for deformable solid dynamics using autoencoder neural networks. We provide a data-driven approach to generating nonlinear reduced spaces for deformation dynamics. In contrast to previous methods using machine learning which accelerate simulation by approximating the time-stepping function, we solve the true equations of motion in the latent-space using a variational formulation of implicit integration. Our approach produces drastically smaller reduced spaces than conventional linear model reduction, improving performance and robustness. Furthermore, our method works well with existing force-approximation cubature methods.
  • Item
    Controlling Motion Blur in Synthetic Long Time Exposures
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Lancelle, Marcel; Dogan, Pelin; Gross, Markus; Alliez, Pierre and Pellacini, Fabio
    In a photo, motion blur can be used as an artistic style to convey motion and to direct attention. In panning or tracking shots, a moving object of interest is followed by the camera during a relatively long exposure. The goal is to get a blurred background while keeping the object sharp. Unfortunately, it can be difficult to impossible to precisely follow the object. Often, many attempts or specialized physical setups are needed. This paper presents a novel approach to create such images. For capturing, the user is only required to take a casually recorded hand-held video that roughly follows the object. Our algorithm then produces a single image which simulates a stabilized long time exposure. This is achieved by first warping all frames such that the object of interest is aligned to a reference frame. Then, optical flow based frame interpolation is used to reduce ghosting artifacts from temporal undersampling. Finally, the frames are averaged to create the result. As our method avoids segmentation and requires little to no user interaction, even challenging sequences can be processed successfully. In addition, artistic control is available in a number of ways. The effect can also be applied to create videos with an exaggerated motion blur. Results are compared with previous methods and ground truth simulations. The effectiveness of our method is demonstrated by applying it to hundreds of datasets. The most interesting results are shown in the paper and in the supplemental material.
  • Item
    What's in a Face? Metric Learning for Face Characterization
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Sendik, Omry; Lischinski, Dani; Cohen-Or, Daniel; Alliez, Pierre and Pellacini, Fabio
    We present a method for determining which facial parts (mouth, nose, etc.) best characterize an individual, given a set of that individual's portraits. We introduce a novel distinctiveness analysis of a set of portraits, which leverages the deep features extracted by a pre-trained face recognition CNN and a hair segmentation FCN, in the context of a weakly supervised metric learning scheme. Our analysis enables the generation of a polarized class activation map (PCAM) for an individual's portrait via a transformation that localizes and amplifies the discriminative regions of the deep feature maps extracted by the aforementioned networks. A user study that we conducted shows that there is a surprisingly good agreement between the face parts that users indicate as characteristic and the face parts automatically selected by our method. We demonstrate a few applications of our method, including determining the most and the least representative portraits among a set of portraits of an individual, and the creation of facial hybrids: portraits that combine the characteristic recognizable facial features of two individuals. Our face characterization analysis is also effective for ranking portraits in order to find an individual's look-alikes (Doppelgängers).
  • Item
    Exploratory Stage Lighting Design using Visual Objectives
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Shimizu, Evan; Paris, Sylvain; Fisher, Matthew; Yumer, Ersin; Fatahalian, Kayvon; Alliez, Pierre and Pellacini, Fabio
    Lighting is a critical element of theater. A lighting designer is responsible for drawing the audience's attention to a specific part of the stage, setting time of day, creating a mood, and conveying emotions. Designers often begin the lighting design process by collecting reference visual imagery that captures different aspects of their artistic intent. Then, they experiment with various lighting options to determine which ideas work best on stage. However, modern stages contain tens to hundreds of lights, and setting each light source's parameters individually to realize an idea is both tedious and requires expert skill. In this paper, we describe an exploratory lighting design tool based on feedback from professional designers. The system extracts abstract visual objectives from reference imagery and applies them to target regions of the stage. Our system can rapidly generate plausible design candidates that embody the visual objectives through a Gibbs sampling method, and present them as a design gallery for rapid exploration and iterative refinement. We demonstrate that the resulting system allows lighting designers of all skill levels to quickly create and communicate complex designs, even for scenes containing many color-changing lights.
  • Item
    A CNN-based Flow Correction Method for Fast Preview
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Xiao, Xiangyun; Wang, Hui; Yang, Xubo; Alliez, Pierre and Pellacini, Fabio
    Eulerian-based smoke simulations are sensitive to the initial parameters and grid resolutions. Due to the numerical dissipation on different levels of the grid and the nonlinearity of the governing equations, the differences in simulation resolutions will result in different results. This makes it challenging for artists to preview the animation results based on low-resolution simulations. In this paper, we propose a learning-based flow correction method for fast previewing based on low-resolution smoke simulations. The main components of our approach lie in a deep convolutional neural network, a grid-layer feature vector and a special loss function. We provide a novel matching model to represent the relationship between low-resolution and high-resolution smoke simulations and correct the overall shape of a low-resolution simulation to closely follow the shape of a high-resolution down-sampled version. We introduce the grid-layer concept to effectively represent the 3D fluid shape, which can also reduce the input and output dimensions. We design a special loss function for the fluid divergence-free constraint in the neural network training process. We have demonstrated the efficacy and the generality of our approach by simulating a diversity of animations deviating from the original training set. In addition, we have integrated our approach into an existing fluid simulation framework to showcase its wide applications.
  • Item
    Practical Person-Specific Eye Rigging
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Bérard, Pascal; Bradley, Derek; Gross, Markus; Beeler, Thabo; Alliez, Pierre and Pellacini, Fabio
    We present a novel parametric eye rig for eye animation, including a new multi-view imaging system that can reconstruct eye poses at submillimeter accuracy to which we fit our new rig. This allows us to accurately estimate person-specific eyeball shape, rotation center, interocular distance, visual axis, and other rig parameters resulting in an animation-ready eye rig. We demonstrate the importance of several aspects of eye modeling that are often overlooked, for example that the visual axis is not identical to the optical axis, that it is important to model rotation about the optical axis, and that the rotation center of the eye should be measured accurately for each person. Since accurate rig fitting requires hand annotation of multi-view imagery for several eye gazes, we additionally propose a more user-friendly ''lightweight'' fitting approach, which leverages an average rig created from several pre-captured accurate rigs. Our lightweight rig fitting method allows for the estimation of eyeball shape and eyeball position given only a single pose with a known look-at point (e.g. looking into a camera) and few manual annotations.