38-Issue 2
https://diglib.eg.org:443/handle/10.2312/2632738
EG 2019 - Conference Issue
2024-03-29T02:08:25Z
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Practical Person-Specific Eye Rigging
https://diglib.eg.org:443/handle/10.1111/cgf13650
Practical Person-Specific Eye Rigging
BĂ©rard, 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.
2019-01-01T00:00:00Z
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A CNN-based Flow Correction Method for Fast Preview
https://diglib.eg.org:443/handle/10.1111/cgf13649
A CNN-based Flow Correction Method for Fast Preview
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.
2019-01-01T00:00:00Z
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Exploratory Stage Lighting Design using Visual Objectives
https://diglib.eg.org:443/handle/10.1111/cgf13648
Exploratory Stage Lighting Design using Visual Objectives
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.
2019-01-01T00:00:00Z
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What's in a Face? Metric Learning for Face Characterization
https://diglib.eg.org:443/handle/10.1111/cgf13647
What's in a Face? Metric Learning for Face Characterization
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).
2019-01-01T00:00:00Z