Physics-Based Animation

Martin Komaritzan, Mario Botsch

We present a novel approach for physics-based character skinning. While maintaining real-time performance it overcomes the well-known artifacts of commonly used geometric skinning approaches, it enables dynamic effects, and it resolves local self-collisions. Our method is based on a two-layer model consisting of rigid bones and an elastic soft tissue layer. This volumetric model is easily and efficiently computed from an input surface mesh of the character and its underlying skeleton. In particular, our method neither requires skinning weights, which are often expensive to compute or tedious to hand-tune, nor a complex volumetric tessellation, which fails for many real-world input meshes due to self-intersections.

Projective Skinning

Dan Casas, Miguel Otaduy

We present a novel method to enrich existing vertex-based human body models by adding soft-tissue dynamics. Our model learns to predict per-vertex 3D offsets, referred to as dynamic blendshapes, that reproduce nonlinear mesh deformation effects as a function of pose information. This enables the synthesis of realistic 3D mesh animations, including soft-tissue effects, using just skeletal motion. At the core of our method there is a neural network regressor trained on high-quality 4D scans from which we extract pose, shape and soft-tissue information. Our regressor uses a novel nonlinear subspace, which we build using an autoencoder, to efficiently compact soft-tissue dynamics information. Once trained, our method can be plugged to existing vertex-based skinning methods with little computational overhead (<10ms), enabling real-time nonlinear dynamics. We qualitatively and quantitatively evaluate our method, and show compelling animations with soft-tissue effects, created using publicly available motion capture datasets

Learning Nonlinear Soft-Tissue Dynamics for Interactive Avatars