Gaussian Garments: Reconstructing Simulation-Ready Clothing with Photorealistic Appearance from Multi-View Video
<|reference_start|>Gaussian Garments: Reconstructing Simulation-Ready Clothing with Photorealistic Appearance from Multi-View Video: 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.<|reference_end|>
@article{rong2024gaussian,
    title={Gaussian Garments: Reconstructing Simulation-Ready Clothing with
  Photorealistic Appearance from Multi-View Video},
    author={Boxiang Rong, Artur Grigorev, Wenbo Wang, Michael J. Black, Bernhard
  Thomaszewski, Christina Tsalicoglou, Otmar Hilliges},
    journal={arXiv preprint arXiv:2409.08189},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08189},
    primaryClass={cs.CV cs.GR}
}