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High-fidelity 3D Human Digitization from Single 2K Resolution Images

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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)
한상훈박민규윤주홍강주미박영재Jeon, Hae-Gon
Issued Date
2023-06-20
Type
Conference Paper
URI
https://scholar.gist.ac.kr/handle/local/21141
Publisher
IEEE/CVF
Citation
IEEE/CVF Conference on Computer Vision and Pattern Recognition
Conference Place
CN
Vancouver Convention Center
Appears in Collections:
Department of AI Convergence > 2. Conference Papers
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