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arXiv · 2007.05661

Deep Patch-based Human Segmentation

Dongbo Zhang·Zheng Fang·Xuequan Lu·Hong Qin·Antonio Robles-Kelly·Chao Zhang·Ying He
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Abstract

3D human segmentation has seen noticeable progress in re-cent years. It, however, still remains a challenge to date. In this paper, weintroduce a deep patch-based method for 3D human segmentation. Wefirst extract a local surface patch for each vertex and then parameterizeit into a 2D grid (or image). We then embed identified shape descriptorsinto the 2D grids which are further fed into the powerful 2D Convolu-tional Neural Network for regressing corresponding semantic labels (e.g.,head, torso). Experiments demonstrate that our method is effective inhuman segmentation, and achieves state-of-the-art accuracy.

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Dongbo Zhang, Zheng Fang, Xuequan Lu, Hong Qin, Antonio Robles-Kelly, Chao Zhang, Ying He. 2020-07-11. Deep Patch-based Human Segmentation. https://arxiv.org/abs/2007.05661

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