@inproceedings{GaussGarments2024,
  title = {Gaussian Garments: Reconstructing Simulation-Ready Clothing with Photo-Realistic Appearance from Multi-View Video},
  booktitle = {International Conference on 3D Vision (3DV)},
  abstract = {We introduce Gaussian Garments, a novel approach for reconstructing realistic-looking, 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.
  },
  month = mar,
  year = {2025},
  slug = {gaussgarments2024},
  author = {Rong, Boxiang and Grigorev, Artur and Wang, Wenbo and Black, Michael J. and Thomaszewski, Bernhard and Tsalicoglou, Christina and Hilliges, Otmar},
  url = {https://ribosome-rbx.github.io/Gaussian-Garments/},
  month_numeric = {3}
}