High-fidelity 3D Human Digitization from Single 2K Resolution Images
- Abstract
- High-quality 3D human body reconstruction requires
high-fidelity and large-scale training data and appropriate
network design that effectively exploits the high-resolution
input images. To tackle these problems, we propose a simple yet effective 3D human digitization method called 2K2K,
which constructs a large-scale 2K human dataset and infers 3D human models from 2K resolution images. The
proposed method separately recovers the global shape of
a human and its details. The low-resolution depth network
predicts the global structure from a low-resolution image,
and the part-wise image-to-normal network predicts the details of the 3D human body structure. The high-resolution
depth network merges the global 3D shape and the detailed
structures to infer the high-resolution front and back side
depth maps. Finally, an off-the-shelf mesh generator reconstructs the full 3D human model, which are available at
https://github.com/SangHunHan92/2K2K. In
addition, we also provide 2,050 3D human models, including
texture maps, 3D joints, and SMPL parameters for research
purposes. In experiments, we demonstrate competitive performance over the recent works on various datasets
- Author(s)
- 한상훈; 박민규; 윤주홍; 강주미; 박영재; Jeon, Hae-Gon
- Issued Date
- 2023-06-20
- Type
- Conference Paper
- URI
- https://scholar.gist.ac.kr/handle/local/21141
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