@inproceedings{5a7029564f144ab1b86cec9d73f65b48,
title = "Deep Patch-Based Human Segmentation",
abstract = "3D human segmentation has seen noticeable progress in recent years. It, however, still remains a challenge to date. In this paper, we introduce a deep patch-based method for 3D human segmentation. We first extract a local surface patch for each vertex and then parameterize it into a 2D grid (or image). We then embed identified shape descriptors into the 2D grids which are further fed into the powerful 2D Convolutional Neural Network for regressing corresponding semantic labels (e.g., head, torso). Experiments demonstrate that our method is effective in human segmentation, and achieves state-of-the-art accuracy.",
keywords = "Deep learning, Human segmentation, Parameterization, Shape descriptors",
author = "Dongbo Zhang and Zheng Fang and Xuequan Lu and Hong Qin and Antonio Robles-Kelly and Chao Zhang and Ying He",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 27th International Conference on Neural Information Processing, ICONIP 2020 ; Conference date: 18-11-2020 Through 22-11-2020",
year = "2020",
doi = "10.1007/978-3-030-63830-6\_20",
language = "英语",
isbn = "9783030638290",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "229--240",
editor = "Haiqin Yang and Kitsuchart Pasupa and Leung, \{Andrew Chi-Sing\} and Kwok, \{James T.\} and Chan, \{Jonathan H.\} and Irwin King",
booktitle = "Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings",
address = "德国",
}