ReLoo: Reconstructing Humans Dressed in Loose Garments from Monocular Video in the Wild
<|reference_start|>ReLoo: Reconstructing Humans Dressed in Loose Garments from Monocular Video in the Wild: While previous years have seen great progress in the 3D reconstruction of humans from monocular videos, few of the state-of-the-art methods are able to handle loose garments that exhibit large non-rigid surface deformations during articulation. This limits the application of such methods to humans that are dressed in standard pants or T-shirts. Our method, ReLoo, overcomes this limitation and reconstructs high-quality 3D models of humans dressed in loose garments from monocular in-the-wild videos. To tackle this problem, we first establish a layered neural human representation that decomposes clothed humans into a neural inner body and outer clothing. On top of the layered neural representation, we further introduce a non-hierarchical virtual bone deformation module for the clothing layer that can freely move, which allows the accurate recovery of non-rigidly deforming loose clothing. A global optimization jointly optimizes the shape, appearance, and deformations of the human body and clothing via multi-layer differentiable volume rendering. To evaluate ReLoo, we record subjects with dynamically deforming garments in a multi-view capture studio. This evaluation, both on existing and our novel dataset, demonstrates ReLoo's clear superiority over prior art on both indoor datasets and in-the-wild videos.<|reference_end|>
@article{guo2024reloo:,
    title={ReLoo: Reconstructing Humans Dressed in Loose Garments from Monocular
  Video in the Wild},
    author={Chen Guo, Tianjian Jiang, Manuel Kaufmann, Chengwei Zheng, Julien
  Valentin, Jie Song, Otmar Hilliges},
    journal={arXiv preprint arXiv:2409.15269},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15269},
    primaryClass={cs.CV}
}