ReMu: Reconstructing Multi-layer 3D Clothed Human from Image Layers

• URL: http://arxiv.org/abs/2508.01381v1
• Date: Sat, 02 Aug 2025 14:24:47 GMT
• Title: ReMu: Reconstructing Multi-layer 3D Clothed Human from Image Layers
• Authors: Onat Vuran, Hsuan-I Ho,
• Abstract summary: We introduce ReMu for reconstructing multi-layer clothed humans in a new setup, Image Layers.<n>We first reconstruct and align each garment layer in a shared coordinate system defined by the canonical body pose.<n>It is worth noting that our method is template-free and category-agnostic, which enables the reconstruction of 3D garments in diverse clothing styles.
• Score: 3.046315755726937
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The reconstruction of multi-layer 3D garments typically requires expensive multi-view capture setups and specialized 3D editing efforts. To support the creation of life-like clothed human avatars, we introduce ReMu for reconstructing multi-layer clothed humans in a new setup, Image Layers, which captures a subject wearing different layers of clothing with a single RGB camera. To reconstruct physically plausible multi-layer 3D garments, a unified 3D representation is necessary to model these garments in a layered manner. Thus, we first reconstruct and align each garment layer in a shared coordinate system defined by the canonical body pose. Afterwards, we introduce a collision-aware optimization process to address interpenetration and further refine the garment boundaries leveraging implicit neural fields. It is worth noting that our method is template-free and category-agnostic, which enables the reconstruction of 3D garments in diverse clothing styles. Through our experiments, we show that our method reconstructs nearly penetration-free 3D clothed humans and achieves competitive performance compared to category-specific methods. Project page: https://eth-ait.github.io/ReMu/

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