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.
@article{arxiv.2409.08189,
title = {Gaussian Garments: Reconstructing Simulation-Ready Clothing with Photorealistic Appearance from Multi-View Video},
author = {Boxiang Rong and Artur Grigorev and Wenbo Wang and Michael J. Black and Bernhard Thomaszewski and Christina Tsalicoglou and Otmar Hilliges},
journal= {arXiv preprint arXiv:2409.08189},
year = {2024}
}