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Existing data-driven methods for garment animation, usually driven by linearskinning, although effective on tight garments, do not handle loose-fittinggarments with complex deformations well. To address these limitations, wedevelop a garment generative model based on deformation decomposition toefficiently simulate loose garment deformation without directly using linearskinning. Specifically, we learn a garment generative space with the proposedgenerative model, where we decouple the latent representation into unposeddeformed garments and dynamic offsets during the decoding stage. With explicitgarment deformations decomposition, our generative model is able to generatecomplex pose-driven deformations on canonical garment shapes. Furthermore, welearn to transfer the body motions and previous state of the garment to thelatent space to regenerate dynamic results. In addition, we introduce a detailenhancement module in an adversarial training setup to learn high-frequencywrinkles. We demonstrate our method outperforms state-of-the-art data-drivenalternatives through extensive experiments and show qualitative andquantitative analysis of results.

Towards Loose-Fitting Garment Animation via Generative Model of Deformation Decomposition