  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.
