PGC: Physics-Based Gaussian Cloth from a Single Pose
Michelle Guo, Matt Jen-Yuan Chiang, Igor Santesteban, Nikolaos Sarafianos, Hsiao-yu Chen, Oshri Halimi, Aljaž Božič, Shunsuke Saito, Jiajun Wu, C. Karen Liu, Tuur Stuyck, Egor Larionov,
We introduce a novel approach to reconstruct simulation-ready garments with intricate appearance. Despite recent advancements, existing methods often struggle to balance the need for accurate garment reconstruction with the ability to generalize to new poses and body shapes or require large amounts of data to achieve this. In contrast, our method only requires a multi-view capture of a single static frame. We represent garments as hybrid mesh-embedded 3D Gaussian splats, where the Gaussians capture near-field shading and high-frequency details, while the mesh encodes far-field albedo and optimized reflectance parameters. We achieve novel pose generalization by exploiting the mesh from our hybrid approach, enabling physics-based simulation and surface rendering techniques, while also capturing fine details with Gaussians that accurately reconstruct garment details. Our optimized garments can be used for simulating garments on novel poses, and garment relighting.
| Paper | |
| Project Page | Webpage |
| Video | YouTube |
BibTeX
@inproceedings{guo2025pgc,
title = {
PGC: Physics-Based Gaussian Cloth from a Single Pose
},
author = {
Guo, Michelle and
Chiang, Matt Jen-Yuan and
Santesteban, Igor and
Sarafianos, Nikolaos
and Chen, Hsiao-yu
and Halimi, Oshri
and Bo{\v{z}}i{\v{c}}, Alja{\v{z}} and
Saito, Shunsuke and
Wu, Jiajun and
Liu, C. Karen and
Stuyck, Tuur and
Larionov, Egor
},
booktitle = {
Proceedings of the IEEE/CVF Conference on
Computer Vision and Pattern Recognition (CVPR)
},
month = {June},
year = {2025}
}