UNIC: Neural Garment Deformation Field for Real-time Clothed Character Animation
Chengfeng Zhao, Junbo Qi, Yulou Liu, Zhiyang Dou, Minchen Li, Taku Komura, Ziwei Liu, Wenping Wang, Yuan Liu

TL;DR
UNIC is a neural deformation field method enabling real-time, high-quality garment animation for virtual characters, focusing on instance-specific learning to handle complex meshes efficiently.
Contribution
Introduces UNIC, a neural deformation field approach that animates garments in real time without requiring generalization to new garments, improving quality and efficiency.
Findings
Outperforms baseline methods in deformation quality.
Operates efficiently for real-time applications.
Effectively handles complex garment topologies.
Abstract
Simulating physically realistic garment deformations is an essential task for virtual immersive experience, which is often achieved by physics simulation methods. However, these methods are typically time-consuming, computationally demanding, and require costly hardware, which is not suitable for real-time applications. Recent learning-based methods tried to resolve this problem by training graph neural networks to learn the garment deformation on vertices, which, however, fail to capture the intricate deformation of complex garment meshes with complex topologies. In this paper, we introduce a novel neural deformation field-based method, named UNIC, to animate the garments of an avatar in real time, given the motion sequences. Our key idea is to learn the instance-specific neural deformation field to animate the garment meshes. Such an instance-specific learning scheme does not require…
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Taxonomy
Topics3D Shape Modeling and Analysis · Generative Adversarial Networks and Image Synthesis · Human Motion and Animation
