将人体与服装解耦，实现自由换装的3D虚拟试穿。
Gaussian Wardrobe: Compositional 3D Gaussian Avatars for Free-Form Virtual Try-On
- 用分层高斯表示法分离人体与无形状依赖的服装层。
- 在新姿态合成任务上达到当前最佳性能，动态细节逼真。
- 支持服装跨人物自由迁移，适用于虚拟试衣场景。
我们提出Gaussian Wardrobe，一种从多视角视频中数字化组合式3D神经化身的新框架。现有3D神经化身方法通常将人体与服装视为不可分割的整体，难以捕捉复杂自由形态衣物的动态，且限制了服装在不同个体间的复用。为此，我们开发了一种新型组合式3D高斯表示，从多层自由形态服装中构建化身。核心思路是将神经化身分解为身体与形状无关的服装层，通过多视角视频学习并将其归一化到与体型无关的空间。实验表明，该方法可生成高保真度、高动态真实感的化身，在新姿态合成基准上达到最新最优表现。此外，所学的组合式服装构成灵活的数字衣橱，实现了可自由转移服装至新主体的实用虚拟试穿应用。项目页面：https://ait.ethz.ch/gaussianwardrobe
原文摘要 · Abstract (English)
We introduce Gaussian Wardrobe, a novel framework to digitalize compositional 3D neural avatars from multi-view videos. Existing methods for 3D neural avatars typically treat the human body and clothing as an inseparable entity. However, this paradigm fails to capture the dynamics of complex free-form garments and limits the reuse of clothing across different individuals. To overcome these problems, we develop a novel, compositional 3D Gaussian representation to build avatars from multiple layers of free-form garments. The core of our method is decomposing neural avatars into bodies and layers of shape-agnostic neural garments. To achieve this, our framework learns to disentangle each garment layer from multi-view videos and canonicalizes it into a shape-independent space. In experiments, our method models photorealistic avatars with high-fidelity dynamics, achieving new state-of-the-art performance on novel pose synthesis benchmarks. In addition, we demonstrate that the learned compositional garments contribute to a versatile digital wardrobe, enabling a practical virtual try-on application where clothing can be freely transferred to new subjects. Project page: https://ait.ethz.ch/gaussianwardrobe
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