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- Title
基于图卷积网络的非参数化三维人体重建.
- Authors
谢昊洋; 钟跃崎
- Abstract
A non-parametric 3D human body reconstruction method based on Graph Convolutional Network (GCN), which does not depend on any existing parametric human body model, was proposed in this paper to improve the precision of reconstruction and make the procedure more controllable. The proposed method only required mask image( s) and a small of anthropometric measurements of a body shape as input and regresses the 3D coordinates as output directly, whose essence was to employ the graph convolutional operator to deform the built-in body template. Experimental results demonstrate that by explicitly integrating the anthropometric sizes into the network with a properly designed loss function, the accuracy of the reconstruction is greatly improved, all anthropometric errors are less than 1 cm, and the reconstruction result is better than other related methods as well.
- Subjects
HUMAN body; SHAPE measurement; IMAGE reconstruction algorithms; SIZE
- Publication
Wool Textile Journal, 2021, Vol 49, Issue 4, p18
- ISSN
1003-1456
- Publication type
Article
- DOI
10.19333/j.mfkj.20210200807