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.

Palette-based image recoloring is one of the popular color manipulation methods by transforming dominant scene colors into corresponding user-specific colors intuitively. Because conventional palette extraction methods are based only on global color distribution, they cannot deal with local distinctive colors, which can cause not only the degraded result of image recoloring, but also the limitation of user selection. In this paper, we propose a new palette extraction method by iterative palette partitioning based on cluster validation. To find candidate colors for palette partitioning, we compute patch uniformity for local patches. For image recoloring with extracted palette, we use a statistics-based color transfer method by considering the pixel-wise weight from palette colors. In the experiments, our automatic or user-specific palette extraction shows more plausible palette representation than previous methods. Furthermore, image recoloring results show the effectiveness of the proposed method in terms of quality of color transfer and user experience.