Gaussian Wardrobe: Compositional 3D Gaussian Avatars for Free-Form Virtual Try-On

• URL: http://arxiv.org/abs/2603.04290v2
• Date: Thu, 05 Mar 2026 15:37:00 GMT
• Title: Gaussian Wardrobe: Compositional 3D Gaussian Avatars for Free-Form Virtual Try-On
• Authors: Zhiyi Chen, Hsuan-I Ho, Tianjian Jiang, Jie Song, Manuel Kaufmann, Chen Guo,
• Abstract summary: We introduce a novel framework to digitalize compositional 3D neural avatars from multi-view videos.<n>The core of our method is decomposing neural avatars into bodies and layers of shape-agnostic neural garments.<n>In experiments, our method models photorealistic avatars with high-fidelity dynamics.
• Score: 19.50809753297068
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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