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.

翻译：我们提出高斯服装，一种从多视角视频重建逼真仿真就绪服装资产的新方法。我们的方法通过结合三维网格与编码颜色及高频表面细节的高斯纹理来表示服装。该表示能够将服装几何精确配准到多视角视频，并有助于将反照率纹理与光照效应解耦。此外，我们展示了如何微调预训练的图神经网络以复现每件服装的真实物理行为。重建的高斯服装可自动组合成多件套服装，并通过微调后的图神经网络进行动画驱动。