2019/03/17 by Igor Santesteban, Santesteban, Igor, Miguel A. Otaduy +4 · 8 citations
Computer Science · Engineering · #3D Shape Modeling and Analysis #Computer Graphics and Visualization Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Motion and Animation #cs.CV
paper · pdf · doi:10.48550/arxiv.1903.07190
Eurographics 2019
arxiv created 2019/03/17 · openalex publication_date 2019/03/17 · arxiv updated 2019/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper presents a learning-based clothing animation method for highly efficient virtual try-on simulation. Given a garment, we preprocess a rich database of physically-based dressed character simulations, for multiple body shapes and animations. Then, using this database, we train a learning-based model of cloth drape and wrinkles, as a function of body shape and dynamics. We propose a model that separates global garment fit, due to body shape, from local garment wrinkles, due to both pose dynamics and body shape. We use a recurrent neural network to regress garment wrinkles, and we achieve highly plausible nonlinear effects, in contrast to the blending artifacts suffered by previous methods. At runtime, dynamic virtual try-on animations are produced in just a few milliseconds for garments with thousands of triangles. We show qualitative and quantitative analysis of results