@inproceedings{90adcb6f20c84df1a570f2bbf0300173,
title = "SFNet: Clothed Human 3D Reconstruction via Single Side-To-Front View RGB-D Image",
abstract = "Front-view human information is critical for reconstructing a detailed 3D human body from a single RGB/RGB-D image. However, we sometimes struggle to access the front-view portrait in practice. Thus, in this work, we propose a bidirectional network (SFNet), one branch to transform side-view RGB image to front-view and another to transform side-view depth image to front-view. Since normal maps typically encode more 3D surface detail information than depth maps, we leverage an adversarial learning framework conditioned on normal maps to improve the performance of predicting front-view depth. Our method is end-To-end trainable, resulting in high fidelity front-view RGB-D estimation and 3D reconstruction.",
keywords = "3D reconstruction, bidirectional network, front-view, nor-mal map, RGB-D",
author = "Xing Li and Yangyu Fan and Di Xu and Wenqing He and Guoyun Lv and Shiya Liu",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 8th International Conference on Virtual Reality, ICVR 2022 ; Conference date: 26-05-2022 Through 28-05-2022",
year = "2022",
doi = "10.1109/ICVR55215.2022.9848377",
language = "英语",
series = "International Conference on Virtual Rehabilitation, ICVR",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "15--20",
booktitle = "2022 8th International Conference on Virtual Reality, ICVR 2022",
}