Example-based Real-time Clothing Synthesis for Virtual Agents

TL;DR

This paper introduces a real-time clothing synthesis method for virtual agents that efficiently models clothing deformation based on body shape and pose, enabling high-fidelity animation at over 50 FPS.

Contribution

The method factorizes clothing deformation into pose and shape components, allowing generalization and efficient synthesis for diverse virtual characters.

Findings

Achieves over 50 FPS on CPU for detailed clothing animation

Outperforms conventional linear blend skinning in user perception tests

Supports high-dimensional shape and pose variations in clothing modeling

Abstract

We present a real-time cloth animation method for dressing virtual humans of various shapes and poses. Our approach formulates the clothing deformation as a high-dimensional function of body shape parameters and pose parameters. In order to accelerate the computation, our formulation factorizes the clothing deformation into two independent components: the deformation introduced by body pose variation (Clothing Pose Model) and the deformation from body shape variation (Clothing Shape Model). Furthermore, we sample and cluster the poses spanning the entire pose space and use those clusters to efficiently calculate the anchoring points. We also introduce a sensitivity-based distance measurement to both find nearby anchoring points and evaluate their contributions to the final animation. Given a query shape and pose of the virtual agent, we synthesize the resulting clothing deformation by blending the Taylor expansion results of nearby anchoring points. Compared to previous methods, our approach is general and able to add the shape dimension to any clothing pose model. %and therefore it is more general. Furthermore, we can animate clothing represented with tens of thousands of vertices at 50+ FPS on a CPU. Moreover, our example database is more representative and can be generated in parallel, and thereby saves the training time. We also conduct a user evaluation and show that our method can improve a user's perception of dressed virtual agents in an immersive virtual environment compared to a conventional linear blend skinning method.