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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