Jang, Jae Won Kwon, Young Chan Lim, Hwasup Choi, Ouk 2024-01-19T18:33:29Z 2024-01-19T18:33:29Z 2021-09-05 2019-12 2169-3536 https://pubs.kist.re.kr/handle/201004/119253 Three-dimensional human shape reconstruction is important in many applications, such as virtual or augmented reality (VR/AR), virtual clothing fitting, and healthcare. In this paper, we propose a learning-based method for reconstructing a whole-body point cloud from a single front-view human-depth image. Because actual depth images typically suffer from noise and missing data, an accurate point cloud cannot be reasonably obtained by simply predicting a back-view depth image. To solve this problem, we propose to use convolutional neural networks that not only predict a back-view depth image but also refine the input front-view depth image. To train the networks, we propose a carefully designed method for generating synthetic but realistic human-depth images with noise and missing data. Experiments show that the proposed method is effective for obtaining seamless whole-body point clouds. In addition, the experiments show that the networks trained on the synthetic depth images are ready for application to actual depth images. English IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC HUMAN BODIES RECONSTRUCTION SHAPE CNN-Based Denoising, Completion, and Prediction of Whole-Body Human-Depth Images Article 10.1109/ACCESS.2019.2957862 1 IEEE ACCESS, v.7, pp.175842 - 175856 IEEE ACCESS 7 175842 175856 scie scopus 000509399500055 2-s2.0-85076969995 Computer Science, Information Systems Engineering, Electrical & Electronic Telecommunications Computer Science Engineering Telecommunications Article HUMAN BODIES RECONSTRUCTION SHAPE 3D human shape convolutional neural networks deep learning single depth image synthetic data generation