SCA '23: Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation

This paper presents a new mass and (local) momentum conserving scheme for shallow water equations that are discretized in conservation form. In comparison to conventional techniques, such as the semi-Lagrangian scheme and its conservative variant, our approach offers noticeably improved visual animation of wave dynamics.

We present a method for controlling topology morphing physically simulated elastoplastic objects. Our method utilizes space-time control of differentiable material point method simulation via per-particle deformation gradients. We base our simulation environment on the material point method due to its natural ability to handle topology change. Our method can generate short animations or produce indefinitely long sequences via a novel chained optimization technique.

Generating realistic Sign Language using signing avatars is a challenging task that typically involves synthesis using either procedural or pre-animated techniques like motion capture or artistic editing of signs. However, combining these two approaches is difficult. In this work, we propose a novel method for generating intermediate poses in a multi-track representation of a sign language discourse. The proposed method uses procedural generation with artistic techniques to prioritize certain aspects of the generated poses while sacrificing others to improve the overall consistency of the representation. The system is implemented as an add-on in Blender, an open-source 3D toolkit.

There has recently been an explosion of interest in creating large-scale shared virtual spaces for multiplayer content. However, rendering player-controllable avatars in real-time creates latency issues when scaling to thousands of players. We introduce a human audience video dataset to support applications in deep learning-based 2D video audience simulation, bypassing the need for background 3D virtual humans. This dataset consists of YouTube videos that depict audiences with diverse lighting conditions, color, dress, and movement patterns. We describe the dataset statistics, our implicit data collection strategy, and audience video extraction pipeline. We apply deep learning tasks on this data based on video prediction techniques, and propose a novel method for 2D audience simulations.

We present a rigid body animation technique which prevents solids from interpenetrating, dissipates energy through friction, and propagates shocks through contacts. We employ the Alternating Direction Method of Multipliers (ADMM) to couple non-smooth Coulomb friction with impact propagation, allowing efficient and accurate non-smooth dynamics along with a correct transmission of impacts through assemblies of rigid bodies. We further extend our method to model adhesion, dynamic friction and lubricated contact.