Get 3D human in video by self-supervised scene decomposition without prior datasets with Vid2Avatar
Get 3D human in video by self-supervised scene decomposition without prior datasets with Vid2Avatar
Vid2Avatar: 3D Avatar Reconstruction from Videos in the Wild via Self-supervised Scene Decomposition
arXiv paper abstract https://arxiv.org/abs/2302.11566
arXiv PDF paper https://arxiv.org/pdf/2302.11566.pdf
Project page https://moygcc.github.io/vid2avatar
YouTube https://youtu.be/EGi47YeIeGQ
… present Vid2Avatar, a method to learn human avatars from monocular in-the-wild videos.
… Solving it requires accurately separating humans from arbitrary backgrounds … reconstructing detailed 3D surface from short video sequences
… method does not require any groundtruth supervision or priors extracted from large datasets of clothed human scans, nor … rely on any external segmentation modules.
… solves the tasks of scene decomposition and surface reconstruction directly in 3D by modeling both the human and the background in the scene jointly, parameterized via two separate neural fields.
… define a temporally consistent human representation in canonical space and formulate a global optimization over the background model, the canonical human shape and texture, and per-frame human pose parameters.
… on publicly available datasets and show improvements over prior art.
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