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 register 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 achieves competitive performance compared to category-specific methods and reconstructs penetration-free 3D clothed humans.
Given a set of k-layered Image Layers Ik, we register SMPL-X body models B and reconstruct watertight meshes M from images.
We then segment out 3D garments S through multi-view parsing.
Next, we deform 3D meshes to the canonical body pose Bc through inverse LBS.
These aligned garments are optimized to remove inter-layer penetrations as M'.
Finally, we fit implicit neural fields f to refine the garment surface geometry and boundaries.
@inproceedings{vuran2025remu,
title = {ReMu: Reconstructing Multi-layer 3D Clothed Human from Images},
author = {Vuran, Onat and Ho, Hsuan-I},
booktitle = {British Machine Vision Conference (BMVC)},
year = {2025}
}