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arXiv · 2409.08189

Gaussian Garments: Reconstructing Simulation-Ready Clothing with Photorealistic Appearance from Multi-View Video

Boxiang Rong·Artur Grigorev·Wenbo Wang·Michael J. Black·Bernhard Thomaszewski·Christina Tsalicoglou·Otmar Hilliges
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Abstract

We introduce Gaussian Garments, a novel approach for reconstructing realistic simulation-ready garment assets from multi-view videos. Our method represents garments with a combination of a 3D mesh and a Gaussian texture that encodes both the color and high-frequency surface details. This representation enables accurate registration of garment geometries to multi-view videos and helps disentangle albedo textures from lighting effects. Furthermore, we demonstrate how a pre-trained graph neural network (GNN) can be fine-tuned to replicate the real behavior of each garment. The reconstructed Gaussian Garments can be automatically combined into multi-garment outfits and animated with the fine-tuned GNN.

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Boxiang Rong, Artur Grigorev, Wenbo Wang, Michael J. Black, Bernhard Thomaszewski, Christina Tsalicoglou, Otmar Hilliges. 2024-09-12. Gaussian Garments: Reconstructing Simulation-Ready Clothing with Photorealistic Appearance from Multi-View Video. https://arxiv.org/abs/2409.08189

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