Cloth Simulation has been used widely in many industries such as fashion (design, online retail, etc.) and entertainment (games, movies, etc. ). However, realistic and fast cloth simulation is still a vastly open problem. For instance, realistic garment simulation is very difficult due to the low speed of high-fidelity simulation methods. This project aims to combine state-of-the-art theoretical research in cloth simulation, computer graphics and machine learning to target this problem. It looks into using new methods in physics-inspired machine learning for high-fidelity fabrics models. It investigates how physics and machine learning models can be combined to automatically simulate fabrics with desired properties, such as to mimic real fabrics.
Abstract
Resources
-
Fine-grained differentiable physics: a yarn-level model for fabrics.
 
The International Conference on Learning Representations (ICLR).
2022
Conference
 Paper    Code    Video    Presentation    Poster   BibTex @inproceedings{gong2022fine, author = {Deshan Gong and Zhanxing Zhu and Andrew J Bulpitt and He Wang}, title = {Fine-grained differentiable physics: a yarn-level model for fabrics}, booktitle = {The International Conference on Learning Representations (ICLR)}, year = {2022} }
, , , .