SCA 2024 - Posters

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https://diglib.eg.org/handle/10.2312/3607034

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Adaptive Sampling for Simulating Granular Materials
(The Eurographics Association, 2024) Gupta, Samraat; Keyser, John; Zordan, Victor
We present a method for generating simulations of granular materials more quickly within a position based dynamics framework. We do this by combining an adaptive particle sampling scheme with an upsampling approach. This allows for faster simulations in interactive applications, while maintaining visual resolution. Particles are merged or split based on their distance from the boundary, allowing for high details in areas of importance such as the surface and edges. Merging particles into a single particle reduces the number of particles for which collisions have to be simulated, thus reducing the overall simulation time. The adaptive sampling technique is then combined with an upsampling scheme that gives the coarser particle simulation the appearance of much finer resolution.
Art-directable Expressive Oscillation Behavior for Rigged Characters
(The Eurographics Association, 2024) Salem, Karim; Rohmer, Damien; Kalyanasundaram, Niranjan; Zordan, Victor; Zordan, Victor
We propose a method to enrich the animation of rigged characters with exaggerated oscillation behavior triggered by a sudden velocity change. Our approach is based on the extension of Velocity Skinning to space-time deformation, where the deformation is computed from a time-filtered version of the angular velocity of the skeleton. The time-filter is easy to parameterize by artists with respect to frequency, magnitude and attenuation while remaining fully compatible with standard rigged character.
Brittle Fracture Animation with VQ-VAE-Based Generative Method
(The Eurographics Association, 2024) Huang, Yuhang; Kanai, Takashi; Zordan, Victor
We propose a novel learning-based approach for predicting fractured shapes based on collision dynamics at run-time and seamlessly integrating realistic brittle fracture animations with rigid-body simulations. Our method utilizes BEM brittle fracture simulations to create training data. We introduce generative geometric segmentation, distinct from instance and semantic segmentation, to represent 3D fracture shapes. We adopt the concept of a neural discrete representation learning framework to optimize multiple discrete fractured patterns with a continuous latent code. Additionally, we propose a novel SDF-based cagecutting method to create fragments by cutting the original shape with the predicted fracture pattern. Our experimental results demonstrate that our approach can generate significantly more detailed brittle fractures compared to existing techniques, while reducing computational time typically when compared to traditional simulation methods at comparable resolutions.
Data-driven Friction for Real-time Applications
(The Eurographics Association, 2024) Nassif, Loïc; Zoubir, O.; Andrews, Sheldon; Kry, Paul G.; Zordan, Victor
We present a novel data-driven approach for simulating friction between rigid bodies that captures the rich diversity of frictional behaviors that arises due to the complex interactions of micro-asperities of different surfaces. Rather than performing detailed simulations with expensive collision detection, we parameterize our friction model based on aggregate features of pairs of surfaces, such as the distribution of normals from each surfaces, which may be easily computed from a texture-based embedding. Our data-driven model is constructed by conducting real-world planar pushing experiments that capture the friction behavior of many different material pairs, and we then fit this data using a Gaussian process (GP). The trained GP model is then evaluated in a real-time simulation and used to update the limit surface used by the contact solver.
A Differentiable Material Point Method Framework for Shape Morphing
(The Eurographics Association, 2024) Xu, Michael; Song, Chang Yong; Levin, David; Hyde, David; Zordan, Victor
We present a novel, physically-based morphing technique for elastic shapes, leveraging the differentiable material point method (MPM) with space-time control through per-particle deformation gradients to accommodate complex topology changes. This approach, grounded in MPM's natural handling of dynamic topologies, is enhanced by a chained iterative optimization technique, allowing for the creation of both succinct and extended morphing sequences that maintain coherence over time. Demonstrated across various challenging scenarios, our method is able to produce detailed elastic deformation and topology transitions, all grounded within our physics-based simulation framework.
Eurographics/ ACM SIGGRAPH Symposium on Computer Animation - Posters: Frontmatter
(The Eurographics Association, 2024) Zordan, Victor; Zordan, Victor
Fast Simulation of Viscous Lava Flow Using Green's Functions as a Smoothing Kernel
(The Eurographics Association, 2024) Kedadry, Yannis; Cordonnier, Guillaume; Zordan, Victor
We present a novel approach to simulate large-scale lava flow in real-time. We use a depth-averaged model from numerical vulcanology to simplify the problem to 2.5D using a single layer of particle with thickness. Yet, lava flow simulation is challenging due to its strong viscosity which introduces computational instabilities. We solve the associated partial differential equations with approximated Green's functions and observe that this solution acts as a smoothing kernel. We use this kernel to diffuse the velocity based on Smoothed Particle Hydrodynamics. This yields a representation of the velocity that implicitly accounts for horizontal viscosity which is otherwise neglected in standard depth-average models. We demonstrate that our method efficiently simulates large-scale lava flows in real-time.
Learning Climbing Controllers for Physics-Based Characters
(The Eurographics Association, 2024) Kang, Kyungwon; Gu, Taehong; Kwon, Taesoo; Zordan, Victor
We propose a physics-based climbing controller that consists of two learning stages. Firstly, a hanging policy is trained to grasp holds in a natural posture. Once the policy is obtained, it is used to extract the positions of the holds, postures, and grip states, thus forming a dataset of favorable hanging poses. Subsequently, a climbing policy is trained to execute actual climbing maneuvers using this hanging state dataset. The climbing policy allows the character to move to the target location using limbs more evenly. Experiments have shown that the proposed method can effectively explore the space of good postures for climbing.
Markerless Multi-view Multi-person Tracking for Combat Sports
(The Eurographics Association, 2024) Feiz, Hossein; Labbé, David; Andrews, Sheldon; Zordan, Victor
We introduce a novel framework for 3D pose estimation in combat sports. Utilizing a sparse multi-camera setup, our approach employs a computer vision-based tracker to extract 2D pose predictions from each camera view, enforcing consistent tracking targets across views with epipolar constraints and long-term video object segmentation. Through a top-down transformerbased approach, we ensure high-quality 2D pose extraction. We estimate the 3D position via weighted triangulation, spline fitting and extended Kalman filtering. By employing kinematic optimization and physics-based trajectory refinement, we achieve state-of-the-art accuracy and robustness under challenging conditions such as occlusion and rapid movements. Experimental validation on diverse datasets, including a custom dataset featuring elite boxers, underscores the effectiveness of our approach. Additionally, we contribute a valuable sparring video dataset to advance research in multi-person tracking for sports.
Neural Implicit Reduced Fluid Simulation
(The Eurographics Association, 2024) Tao, Yuanyuan; Puhachov, Ivan; Nowrouzezahrai, Derek; Kry, Paul; Zordan, Victor
High-fidelity simulation of fluid dynamics is challenging because of the high dimensional state data needed to capture fine details and the large computational cost associated with advancing the system in time. We present neural implicit reduced fluid simulation (NIRFS), a reduced fluid simulation technique that combines a neural-implicit representation of fluid shapes and a neural ordinary differential equation to model the dynamics of fluid in the reduced latent space. Trajectories for NIRFS can be computed at very little cost in comparison to simulations for generating training data, while preserving many of the fine details. We show that this approach can work well, capturing the shapes and dynamics involved in a variety of scenarios with constrained initial conditions, e.g., droplet-droplet collisions, crown splashes, and fluid slosh in a container. In each scenario, we learn the latent implicit representation of fluid shapes with a deep-network signed distance function, as well as the energy function and parameters of a damped Hamiltonian system, which helps guarantee desirable properties of the latent dynamics. To ensure that latent shape representations form smooth and physically meaningful trajectories, we simultaneously learn the latent representation and dynamics. We evaluate novel simulations for conservation of volume and momentum conservation, discuss design decisions, and demonstrate an application of our method to fluid control.
Organic Brushstrokes
(The Eurographics Association, 2024) Joel, William J.; Zordan, Victor
The main goal of this project was to determine (a) if operations could be developed for brushstrokes like those generally performed on 2D geometric objects (scale, rotate, translate, copy, etc.) and (b) if new operations could be developed that provide users new ways to manipulate brushstroke paths.
Rigid Body Adversarial Attacks
(The Eurographics Association, 2024) Ramakrishnan, Aravind; Levin, David I. W.; Jacobson, Alec; Zordan, Victor
When simulating stiff objects in graphics and robotics, rigid body simulators are extremely popular and are often used in lieu of more accurate deformable simulators due to their performance and simplicity. But even very stiff materials are deformable at some scale, affecting how objects respond to contacts and how internal stresses propagate through an object. Moreover, spatial variations of material properties in an object are not captured by the object-level parameters of a rigid body simulator: mass, center of mass and moment of inertia. In this paper, we propose using optimization techniques to construct adversarial objects using physically reasonable materials, which will behave identically to a reference object in rigid body simulation, but maximally different in more physically accurate deformable simulation. As rigid body simulators use only the collision geometry and the moments of the object, the adversarial objects require identical external geometry and first three moments of mass to the reference object. We demonstrate our method by constructing several adversarial objects and comparing the results of their simulations with their reference using the commercially available simulators POLYFEM and BULLET.
Smoothed-Hinge Model for Cloth Simulation
(The Eurographics Association, 2024) Liang, Qixin; Zordan, Victor
We present a smoothed-hinge model for cloth simulation, incorporating a smoothed-hinge membrane (SHM) and a smoothedhinge bending (SHB) component, both found on a triangle-centered elemental patch. SHB derives the directional curvatures across the hinge edges and transforms them into the curvature components used in the continuum shell such that the bending energy can be computed accordingly. Using the corotational method with a small strain/curvature assumption ensures a constant Hessian matrix which enhances the efficiency and stability of implicit solvers. The SHM model, based on a quadratic interpolation scheme, samples membrane strains at the mid-points of hinge edges to address sharp creasing artifacts arising from the locking issue, offering smoother gradients on hinge edges compared to traditional constant strain triangle elements. Incremental potential contact (IPC) manages contact and friction. Our model enriches the family of computational models for realistic cloth simulation, providing a stable and accurate method applicable across diverse clothing scenarios.

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