  Existing data-driven methods for garment animation, usually driven by linear
skinning, although effective on tight garments, do not handle loose-fitting
garments with complex deformations well. To address these limitations, we
develop a garment generative model based on deformation decomposition to
efficiently simulate loose garment deformation without directly using linear
skinning. Specifically, we learn a garment generative space with the proposed
generative model, where we decouple the latent representation into unposed
deformed garments and dynamic offsets during the decoding stage. With explicit
garment deformations decomposition, our generative model is able to generate
complex pose-driven deformations on canonical garment shapes. Furthermore, we
learn to transfer the body motions and previous state of the garment to the
latent space to regenerate dynamic results. In addition, we introduce a detail
enhancement module in an adversarial training setup to learn high-frequency
wrinkles. We demonstrate our method outperforms state-of-the-art data-driven
alternatives through extensive experiments and show qualitative and
quantitative analysis of results.
