PGC: Physics-Based Gaussian Cloth from a Single Pose
March 26, 2025 Β· Declared Dead Β· π Computer Vision and Pattern Recognition
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Authors
Michelle Guo, Matt Jen-Yuan Chiang, Igor Santesteban, Nikolaos Sarafianos, Hsiao-yu Chen, Oshri Halimi, AljaΕΎ BoΕΎiΔ, Shunsuke Saito, Jiajun Wu, C. Karen Liu, Tuur Stuyck, Egor Larionov
arXiv ID
2503.20779
Category
cs.GR: Graphics
Citations
3
Venue
Computer Vision and Pattern Recognition
Last Checked
4 months ago
Abstract
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. Project page: https://phys-gaussian-cloth.github.io .
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