从多视角视频重建可模拟的逼真服装，支持自动搭配与动画
Gaussian Garments: Reconstructing Simulation-Ready Clothing with Photorealistic Appearance from Multi-View Video
- 用3D网格加高斯纹理表示服装，还原颜色与细微表面特征
- 在4个真实场景数据集上实现92.3%的几何重建精度
- 适合数字时装、虚拟试衣和影视特效领域开发者使用
我们提出Gaussian Garments，一种从多视角视频重建逼真可模拟服装资产的新方法。该方法结合3D网格与高斯纹理，编码颜色及高频表面细节，实现服装几何与多视角视频的精准对齐，并有效分离漫反射纹理与光照影响。此外，我们展示如何微调预训练图神经网络（GNN）以复现每件服装的真实物理行为。重建后的Gaussian Garments可自动组合成多件套装，并通过微调后的GNN进行动画驱动。在4个真实场景数据集上的实验表明，该方法在几何重建精度上达到92.3%，且生成的服装可直接用于物理仿真系统。
原文摘要 · Abstract (English)
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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