Volume 43 (2024)

Permanent URI for this community

https://diglib.eg.org/handle/10.2312/3606941

Browse

Now showing 1 - 20 of 168

1-Lipschitz Neural Distance Fields
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Coiffier, Guillaume; Béthune, Louis; Hu, Ruizhen; Lefebvre, Sylvain
Neural implicit surfaces are a promising tool for geometry processing that represent a solid object as the zero level set of a neural network. Usually trained to approximate a signed distance function of the considered object, these methods exhibit great visual fidelity and quality near the surface, yet their properties tend to degrade with distance, making geometrical queries hard to perform without the help of complex range analysis techniques. Based on recent advancements in Lipschitz neural networks, we introduce a new method for approximating the signed distance function of a given object. As our neural function is made 1- Lipschitz by construction, it cannot overestimate the distance, which guarantees robustness even far from the surface. Moreover, the 1-Lipschitz constraint allows us to use a different loss function, called the hinge-Kantorovitch-Rubinstein loss, which pushes the gradient as close to unit-norm as possible, thus reducing computation costs in iterative queries. As this loss function only needs a rough estimate of occupancy to be optimized, this means that the true distance function need not to be known. We are therefore able to compute neural implicit representations of even bad quality geometry such as noisy point clouds or triangle soups. We demonstrate that our methods is able to approximate the distance function of any closed or open surfaces or curves in the plane or in space, while still allowing sphere tracing or closest point projections to be performed robustly.
3D Reconstruction and Semantic Modeling of Eyelashes
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Kerbiriou, Glenn; Avril, Quentin; Marchal, Maud; Bermano, Amit H.; Kalogerakis, Evangelos
High-fidelity digital human modeling has become crucial in various applications, including gaming, visual effects and virtual reality. Despite the significant impact of eyelashes on facial aesthetics, their reconstruction and modeling have been largely unexplored. In this paper, we introduce the first data-driven generative model of eyelashes based on semantic features. This model is derived from real data by introducing a new 3D eyelash reconstruction method based on multi-view images. The reconstructed data is made available which constitutes the first dataset of 3D eyelashes ever published. Through an innovative extraction process, we determine the features of any set of eyelashes, and present detailed descriptive statistics of human eyelashes shapes. The proposed eyelashes model, which exclusively relies on semantic parameters, effectively captures the appearance of a set of eyelashes. Results show that the proposed model enables interactive, intuitive and realistic eyelashes modeling for non-experts, enriching avatar creation and synthetic data generation pipelines.
ADAPT: AI-Driven Artefact Purging Technique for IMU Based Motion Capture
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Schreiner, Paul; Netterstrøm, Rasmus; Yin, Hang; Darkner, Sune; Erleben, Kenny; Skouras, Melina; Wang, He
While IMU based motion capture offers a cost-effective alternative to premium camera-based systems, it often falls short in matching the latter's realism. Common distortions, such as self-penetrating body parts, foot skating, and floating, limit the usability of these systems, particularly for high-end users. To address this, we employed reinforcement learning to train an AI agent that mimics erroneous sample motion. Since our agent operates within a simulated environment, it inherently avoids generating these distortions since it must adhere to the laws of physics. Impressively, the agent manages to mimic the sample motions while preserving their distinctive characteristics. We assessed our method's efficacy across various types of input data, showcasing an ideal blend of artefact-laden IMU-based data with high-grade optical motion capture data. Furthermore, we compared the configuration of observation and action spaces with other implementations, pinpointing the most suitable configuration for our purposes. All our models underwent rigorous evaluation using a spectrum of quantitative metrics complemented by a qualitative review. These evaluations were performed using a benchmark dataset of IMU-based motion data from actors not included in the training data.
Advances in Data‐Driven Analysis and Synthesis of 3D Indoor Scenes
(© 2024 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024) Patil, Akshay Gadi; Patil, Supriya Gadi; Li, Manyi; Fisher, Matthew; Savva, Manolis; Zhang, Hao; Alliez, Pierre; Wimmer, Michael
This report surveys advances in deep learning‐based modelling techniques that address four different 3D indoor scene analysis tasks, as well as synthesis of 3D indoor scenes. We describe different kinds of representations for indoor scenes, various indoor scene datasets available for research in the aforementioned areas, and discuss notable works employing machine learning models for such scene modelling tasks based on these representations. Specifically, we focus on the and of 3D indoor scenes. With respect to analysis, we focus on four basic scene understanding tasks – 3D object detection, 3D scene segmentation, 3D scene reconstruction and 3D scene similarity. And for synthesis, we mainly discuss neural scene synthesis works, though also highlighting model‐driven methods that allow for human‐centric, progressive scene synthesis. We identify the challenges involved in modelling scenes for these tasks and the kind of machinery that needs to be developed to adapt to the data representation, and the task setting in general. For each of these tasks, we provide a comprehensive summary of the state‐of‐the‐art works across different axes such as the choice of data representation, backbone, evaluation metric, input, output and so on, providing an organized review of the literature. Towards the end, we discuss some interesting research directions that have the potential to make a direct impact on the way users interact and engage with these virtual scene models, making them an integral part of the metaverse.
Advancing Front Surface Mapping
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Livesu, Marco; Bermano, Amit H.; Kalogerakis, Evangelos
We present Advancing Front Mapping (AFM), a novel algorithm for the computation of injective maps to simple planar domains. AFM is inspired by the advancing front meshing paradigm, which is here revisited to operate on two embeddings at once, becoming a tool for compatible mesh generation. AFM extends the capabilities of existing robust approaches, supporting a broader set of embeddings (star-shaped polygons) with a direct approach, without resorting to intermediate constructions. Our method only relies on two topological operators (split and flip) and on the computation of segment intersections, thus permitting to compute a valid embedding without solving any numerical problem. AFM is therefore easy to implement, debug and deploy. This article is mainly focused on the presentation of the compatible advancing front idea and on the demonstration that the algorithm provably converges to an injective map. We also complement our theoretical analysis with an extensive practical validation, executing more than one billion advancing front moves on 36K mapping tasks.
Anisotropy and Cross Fields
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Simons, Lance; Amenta, Nina; Hu, Ruizhen; Lefebvre, Sylvain
We consider a cross field, possibly with singular points of valence 3 or 5, in which all streamlines are finite, and either end on the boundary or form cycles. We show that we can always assign lengths to the two cross field directions to produce an anisotropic orthogonal frame field. There is a one-dimensional family of such length functions, and we optimize within this family so that the two lengths are everywhere as similar as possible. This gives a numerical bound on the minimal anisotropy of any quad mesh exactly following the input cross field. We also show how to remove some limit cycles.
Antarstick: Extracting Snow Height From Time-Lapse Photography
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Lang, Matěj; Mráz, Radoslav; Trtík, Marek; Stoppel, Sergej; Byška, Jan; Kozlikova, Barbora; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
The evolution and accumulation of snow cover are among the most important characteristics influencing Antarctica's climate and biotopes. The changes in Antarctica are also substantially impacting global climate change. Therefore, detailed monitoring of snow evolution is key to understanding such changes. One way to conduct this monitoring is by installing trail cameras in a particular region and then processing the captured information. This option is affordable, but has some drawbacks, such as the fully automatic solution for the extraction of snow height from these images is not feasible. Therefore, it still requires human intervention, manually correcting the inaccurately extracted information. In this paper, we present Antarstick, a tool for visual guidance of the user to potentially wrong values extracted from poor-quality images and support for their interactive correction. This tool allows for much quicker and semi-automated processing of snow height from time-lapse photography.
AutoVizuA11y: A Tool to Automate Screen Reader Accessibility in Charts
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Duarte, Diogo; Costa, Rita; Bizarro, Pedro; Duarte, Carlos; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Charts remain widely inaccessible on the web for users of assistive technologies like screen readers. This is, in part, due to data visualization experts still lacking the experience, knowledge, and time to consistently implement accessible charts. As a result, screen reader users are prevented from accessing information and are forced to resort to tabular alternatives (if available), limiting the insights that they can gather. We worked with both groups to develop AutoVizuA11y, a tool that automates the addition of accessible features to web-based charts. It generates human-like descriptions of the data using a large language model, calculates statistical insights from the data, and provides keyboard navigation between multiple charts and underlying elements. Fifteen screen reader users interacted with charts made accessible with AutoVizuA11y in a usability test, thirteen of which praised the tool for its intuitive design, short learning curve, and rich information. On average, they took 66 seconds to complete each of the eight analytical tasks presented and achieved a success rate of 89%. Through a SUS questionnaire, the participants gave AutoVizuA11y an ''Excellent'' score-83.5/100 points. We also gathered feedback from two data visualization experts who used the tool. They praised the tool availability, ease of use and functionalities, and provided feedback to add AutoVizuA11y support for other technologies in the future.
Auxiliary Features‐Guided Super Resolution for Monte Carlo Rendering
(© 2024 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024) Hou, Qiqi; Liu, Feng; Alliez, Pierre; Wimmer, Michael
This paper investigates super‐resolution to reduce the number of pixels to render and thus speed up Monte Carlo rendering algorithms. While great progress has been made to super‐resolution technologies, it is essentially an ill‐posed problem and cannot recover high‐frequency details in renderings. To address this problem, we exploit high‐resolution auxiliary features to guide super‐resolution of low‐resolution renderings. These high‐resolution auxiliary features can be quickly rendered by a rendering engine and at the same time provide valuable high‐frequency details to assist super‐resolution. To this end, we develop a cross‐modality transformer network that consists of an auxiliary feature branch and a low‐resolution rendering branch. These two branches are designed to fuse high‐resolution auxiliary features with the corresponding low‐resolution rendering. Furthermore, we design Residual Densely Connected Swin Transformer groups to learn to extract representative features to enable high‐quality super‐resolution. Our experiments show that our auxiliary features‐guided super‐resolution method outperforms both super‐resolution methods and Monte Carlo denoising methods in producing high‐quality renderings.
AVA: Towards Autonomous Visualization Agents through Visual Perception-Driven Decision-Making
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Liu, Shusen; Miao, Haichao; Li, Zhimin; Olson, Matthew; Pascucci, Valerio; Bremer, Peer-Timo; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
With recent advances in multi-modal foundation models, the previously text-only large language models (LLM) have evolved to incorporate visual input, opening up unprecedented opportunities for various applications in visualization. Compared to existing work on LLM-based visualization works that generate and control visualization with textual input and output only, the proposed approach explores the utilization of the visual processing ability of multi-modal LLMs to develop Autonomous Visualization Agents (AVAs) that can evaluate the generated visualization and iterate on the result to accomplish user-defined objectives defined through natural language. We propose the first framework for the design of AVAs and present several usage scenarios intended to demonstrate the general applicability of the proposed paradigm. Our preliminary exploration and proof-of-concept agents suggest that this approach can be widely applicable whenever the choices of appropriate visualization parameters require the interpretation of previous visual output. Our study indicates that AVAs represent a general paradigm for designing intelligent visualization systems that can achieve high-level visualization goals, which pave the way for developing expert-level visualization agents in the future.
BallMerge: High-quality Fast Surface Reconstruction via Voronoi Balls
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Parakkat, Amal Dev; Ohrhallinger, Stefan; Eisemann, Elmar; Memari, Pooran; Bermano, Amit H.; Kalogerakis, Evangelos
We introduce a Delaunay-based algorithm for reconstructing the underlying surface of a given set of unstructured points in 3D. The implementation is very simple, and it is designed to work in a parameter-free manner. The solution builds upon the fact that in the continuous case, a closed surface separates the set of maximal empty balls (medial balls) into an interior and exterior. Based on discrete input samples, our reconstructed surface consists of the interface between Voronoi balls, which approximate the interior and exterior medial balls. An initial set of Voronoi balls is iteratively processed, merging Voronoi-ball pairs if they fulfil an overlapping error criterion. Our complete open-source reconstruction pipeline performs up to two quick linear-time passes on the Delaunay complex to output the surface, making it an order of magnitude faster than the state of the art while being competitive in memory usage and often superior in quality. We propose two variants (local and global), which are carefully designed to target two different reconstruction scenarios for watertight surfaces from accurate or noisy samples, as well as real-world scanned data sets, exhibiting noise, outliers, and large areas of missing data. The results of the global variant are, by definition, watertight, suitable for numerical analysis and various applications (e.g., 3D printing). Compared to classical Delaunay-based reconstruction techniques, our method is highly stable and robust to noise and outliers, evidenced via various experiments, including on real-world data with challenges such as scan shadows, outliers, and noise, even without additional preprocessing.
Beyond ExaBricks: GPU Volume Path Tracing of AMR Data
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Zellmann, Stefan; Wu, Qi; Sahistan, Alper; Ma, Kwan-Liu; Wald, Ingo; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Adaptive Mesh Refinement (AMR) is becoming a prevalent data representation for HPC, and thus also for scientific visualization. AMR data is usually cell centric (which imposes numerous challenges), complex, and generally hard to render. Recent work on GPU-accelerated AMR rendering has made much progress towards real-time volume and isosurface rendering of such data, but so far this work has focused exclusively on ray marching, with simple lighting models and without scattering events or global illumination. True high-quality rendering requires a modified approach that is able to trace arbitrary incoherent paths; but this may not be a perfect fit for the types of data structures recently developed for ray marching. In this paper, we describe a novel approach to high-quality path tracing of complex AMR data, with a specific focus on analyzing and comparing different data structures and algorithms to achieve this goal.
Bridge Sampling for Connections via Multiple Scattering Events
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Schüßler, Vincent; Hanika, Johannes; Dachsbacher, Carsten; Garces, Elena; Haines, Eric
Explicit sampling of and connecting to light sources is often essential for reducing variance in Monte Carlo rendering. In dense, forward-scattering participating media, its benefit declines, as significant transport happens over longer multiple-scattering paths around the straight connection to the light. Sampling these paths is challenging, as their contribution is shaped by the product of reciprocal squared distance terms and the phase functions. Previous work demonstrates that sampling several of these terms jointly is crucial. However, these methods are tied to low-order scattering or struggle with highly-peaked phase functions. We present a method for sampling a bridge: a subpath of arbitrary vertex count connecting two vertices. Its probability density is proportional to all phase functions at inner vertices and reciprocal squared distance terms. To achieve this, we importance sample the phase functions first, and subsequently all distances at once. For the latter, we sample an independent, preliminary distance for each edge of the bridge, and afterwards scale the bridge such that it matches the connection distance. The scale factor can be marginalized out analytically to obtain the probability density of the bridge. This approach leads to a simple algorithm and can construct bridges of any vertex count. For the case of one or two inserted vertices, we also show an alternative without scaling or marginalization. For practical path sampling, we present a method to sample the number of bridge vertices whose distribution depends on the connection distance, the phase function, and the collision coefficient. While our importance sampling treats media as homogeneous we demonstrate its effectiveness on heterogeneous media.
CAN: Concept-aligned Neurons for Visual Comparison of Neural Networks
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Li, Mingwei; Jeong, Sangwon; Liu, Shusen; Berger, Matthew; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
We present concept-aligned neurons, or CAN, a visualization design for comparing deep neural networks. The goal of CAN is to support users in understanding the similarities and differences between neural networks, with an emphasis on comparing neuron functionality across different models. To make this comparison intuitive, CAN uses concept-based representations of neurons to visually align models in an interpretable manner. A key feature of CAN is the hierarchical organization of concepts, which permits users to relate sets of neurons at different levels of detail. CAN's visualization is designed to help compare the semantic coverage of neurons, as well as assess the distinctiveness, redundancy, and multi-semantic alignment of neurons or groups of neurons, all at different concept granularity. We demonstrate the generality and effectiveness of CAN by comparing models trained on different datasets, neural networks with different architectures, and models trained for different objectives, e.g. adversarial robustness, and robustness to out-of-distribution data.
Cascading Upper Bounds for Triangle Soup Pompeiu-Hausdorff Distance
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Sacht, Leonardo; Jacobson, Alec; Hu, Ruizhen; Lefebvre, Sylvain
We propose a new method to accurately approximate the Pompeiu-Hausdorff distance from a triangle soup A to another triangle soup B up to a given tolerance. Based on lower and upper bound computations, we discard triangles from A that do not contain the maximizer of the distance to B and subdivide the others for further processing. In contrast to previous methods, we use four upper bounds instead of only one, three of which newly proposed by us. Many triangles are discarded using the simpler bounds, while the most difficult cases are dealt with by the other bounds. Exhaustive testing determines the best ordering of the four upper bounds. A collection of experiments shows that our method is faster than all previous accurate methods in the literature.
CharacterMixer: Rig-Aware Interpolation of 3D Characters
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Zhan, Xiao; Fu, Rao; Ritchie, Daniel; Bermano, Amit H.; Kalogerakis, Evangelos
We present CharacterMixer, a system for blending two rigged 3D characters with different mesh and skeleton topologies while maintaining a rig throughout interpolation. CharacterMixer also enables interpolation during motion for such characters, a novel feature. Interpolation is an important shape editing operation, but prior methods have limitations when applied to rigged characters: they either ignore the rig (making interpolated characters no longer posable) or use a fixed rig and mesh topology. To handle different mesh topologies, CharacterMixer uses a signed distance field (SDF) representation of character shapes, with one SDF per bone. To handle different skeleton topologies, it computes a hierarchical correspondence between source and target character skeletons and interpolates the SDFs of corresponding bones. This correspondence also allows the creation of a single ''unified skeleton'' for posing and animating interpolated characters. We show that CharacterMixer produces qualitatively better interpolation results than two state-of-the-art methods while preserving a rig throughout interpolation. Project page: https://seanxzhan.github.io/projects/CharacterMixer.
ChoreoVis: Planning and Assessing Formations in Dance Choreographies
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Beck, Samuel; Doerr, Nina; Kurzhals, Kuno; Riedlinger, Alexander; Schmierer, Fabian; Sedlmair, Michael; Koch, Steffen; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Sports visualization has developed into an active research field over the last decades. Many approaches focus on analyzing movement data recorded from unstructured situations, such as soccer. For the analysis of choreographed activities like formation dancing, however, the goal differs, as dancers follow specific formations in coordinated movement trajectories. To date, little work exists on how visual analytics methods can support such choreographed performances. To fill this gap, we introduce a new visual approach for planning and assessing dance choreographies. In terms of planning choreographies, we contribute a web application with interactive authoring tools and views for the dancers' positions and orientations, movement trajectories, poses, dance floor utilization, and movement distances. For assessing dancers' real-world movement trajectories, extracted by manual bounding box annotations, we developed a timeline showing aggregated trajectory deviations and a dance floor view for detailed trajectory comparison. Our approach was developed and evaluated in collaboration with dance instructors, showing that introducing visual analytics into this domain promises improvements in training efficiency for the future.
Cinematographic Camera Diffusion Model
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Jiang, Hongda; Wang, Xi; Christie, Marc; Liu, Libin; Chen, Baoquan; Bermano, Amit H.; Kalogerakis, Evangelos
Designing effective camera trajectories in virtual 3D environments is a challenging task even for experienced animators. Despite an elaborate film grammar, forged through years of experience, that enables the specification of camera motions through cinematographic properties (framing, shots sizes, angles, motions), there are endless possibilities in deciding how to place and move cameras with characters. Dealing with these possibilities is part of the complexity of the problem. While numerous techniques have been proposed in the literature (optimization-based solving, encoding of empirical rules, learning from real examples,...), the results either lack variety or ease of control. In this paper, we propose a cinematographic camera diffusion model using a transformer-based architecture to handle temporality and exploit the stochasticity of diffusion models to generate diverse and qualitative trajectories conditioned by high-level textual descriptions. We extend the work by integrating keyframing constraints and the ability to blend naturally between motions using latent interpolation, in a way to augment the degree of control of the designers. We demonstrate the strengths of this text-to-camera motion approach through qualitative and quantitative experiments and gather feedback from professional artists. The code and data are available at https://github.com/jianghd1996/Camera-control.
Computational Smocking through Fabric-Thread Interaction
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Zhou, Ningfeng; Ren, Jing; Sorkine-Hornung, Olga; Bermano, Amit H.; Kalogerakis, Evangelos
We formalize Italian smocking, an intricate embroidery technique that gathers flat fabric into pleats along meandering lines of stitches, resulting in pleats that fold and gather where the stitching veers. In contrast to English smocking, characterized by colorful stitches decorating uniformly shaped pleats, and Canadian smocking, which uses localized knots to form voluminous pleats, Italian smocking permits the fabric to move freely along the stitched threads following curved paths, resulting in complex and unpredictable pleats with highly diverse, irregular structures, achieved simply by pulling on the threads. We introduce a novel method for digital previewing of Italian smocking results, given the thread stitching path as input. Our method uses a coarse-grained mass-spring system to simulate the interaction between the threads and the fabric. This configuration guides the fine-level fabric deformation through an adaptation of the state-of-the-art simulator, C-IPC [LKJ21]. Our method models the general problem of fabric-thread interaction and can be readily adapted to preview Canadian smocking as well.We compare our results to baseline approaches and physical fabrications to demonstrate the accuracy of our method.
Coverage Axis++: Efficient Inner Point Selection for 3D Shape Skeletonization
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Wang, Zimeng; Dou, Zhiyang; Xu, Rui; Lin, Cheng; Liu, Yuan; Long, Xiaoxiao; Xin, Shiqing; Komura, Taku; Yuan, Xiaoming; Wang, Wenping; Hu, Ruizhen; Lefebvre, Sylvain
We introduce Coverage Axis++, a novel and efficient approach to 3D shape skeletonization. The current state-of-the-art approaches for this task often rely on the watertightness of the input [LWS*15; PWG*19; PWG*19] or suffer from substantial computational costs [DLX*22; CD23], thereby limiting their practicality. To address this challenge, Coverage Axis++ proposes a heuristic algorithm to select skeletal points, offering a high-accuracy approximation of the Medial Axis Transform (MAT) while significantly mitigating computational intensity for various shape representations. We introduce a simple yet effective strategy that considers shape coverage, uniformity, and centrality to derive skeletal points. The selection procedure enforces consistency with the shape structure while favoring the dominant medial balls, which thus introduces a compact underlying shape representation in terms of MAT. As a result, Coverage Axis++ allows for skeletonization for various shape representations (e.g., water-tight meshes, triangle soups, point clouds), specification of the number of skeletal points, few hyperparameters, and highly efficient computation with improved reconstruction accuracy. Extensive experiments across a wide range of 3D shapes validate the efficiency and effectiveness of Coverage Axis++. Our codes are available at https://github.com/Frank-ZY-Dou/Coverage_Axis.

Browse

Browsing Volume 43 (2024) by Title

Results Per Page

Sort Options