Name
Abstract | ||
|---|---|---|
We present a novel mesh-based learning approach (N-Cloth) for plausible 3D cloth deformation prediction. Our approach is general and can handle cloth or obstacles represented by triangle meshes with arbitrary topologies. We use graph convolution to transform the cloth and object meshes into a latent space to reduce the non-linearity in the mesh space. Our network can predict the target 3D cloth mesh deformation based on the initial state of the cloth mesh template and the target obstacle mesh. Our approach can handle complex cloth meshes with up to 100K triangles and scenes with various objects corresponding to SMPL humans, non-SMPL humans or rigid bodies. In practice, our approach can be used to generate plausible cloth simulation at 30 - 45 fps on an NVIDIA GeForce RTX 3090 GPU. We highlight its benefits over prior learning-based methods and physically-based cloth simulators. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1111/cgf.14493 | COMPUTER GRAPHICS FORUM |
Keywords | DocType | Volume |
<bold>CCS Concepts</bold>, center dot <bold>Computing methodologies</bold> -> Machine learning, Physical simulation | Journal | 41 |
Issue | ISSN | Citations |
2 | 0167-7055 | 0 |
PageRank | References | Authors |
0.34 | 23 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yudi Li | 1 | 0 | 0.34 |
| Min Tang | 2 | 623 | 51.33 |
| Yun Yang | 3 | 2103 | 150.49 |
| Zi Huang | 4 | 0 | 0.34 |
| Ruofeng Tong | 5 | 466 | 49.69 |
| Shuangcai Yang | 6 | 0 | 0.34 |
| Yao Li | 7 | 0 | 1.35 |
| Dinesh Manocha | 8 | 9551 | 787.40 |