# PGC: Physics-Based Gaussian Cloth from a Single Pose

arXiv: https://arxiv.org/abs/2503.20779 · HTML: https://mlsanity.com/paper/2503.20779/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2025.

## Built on by (most cited first, 11 of 11)

- [Image2Garment: Simulation-ready Garment Generation from a Single Image](https://mlsanity.com/paper/2601.09658/) (2026)
- [Zero-Shot Reconstruction of Animatable 3D Avatars with Cloth Dynamics from a Single Image](https://mlsanity.com/paper/2603.14772/) (2026)
- [NeuROK: Generative 4D Neural Object Kinematics](https://mlsanity.com/paper/2605.30347/) (2026)
- [DAMA: Disentangled Body-Anchored Gaussians for Controllable Multi-Layered Avatars](https://mlsanity.com/paper/2605.21001/) (2026)
- [When 3D Gaussian Splatting Recovers Real Surfaces](https://mlsanity.com/paper/2608.30054/) (2026)
- [Low-Rank Head Avatar Personalization with Registers](https://mlsanity.com/paper/2506.01935/) (2025)
- [CausalGS: Learning Physical Causality of 3D Dynamic Scenes with Gaussian Representations](https://mlsanity.com/paper/2605.10586/) (2026)
- [EgoRelight: Egocentric Human Capture and Illumination Recovery for Relightable and Photoreal Avatar Rendering](https://mlsanity.com/paper/2605.28401/) (2026)
- [Real-Time Physics Simulation with Dynamic Mesh-Gaussian Reconstructions](https://mlsanity.com/paper/2606.00444/) (2026)
- [DR-GS: Physically-Based Deformable and Relightable 2D Gaussians](https://mlsanity.com/paper/2606.29379/) (2026)
- [Stitched Embeddings: A Unified Latent Space for 3D Garments and 2D Patterns](https://mlsanity.com/paper/2607.00829/) (2026)

## Abstract (arXiv)

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 .