TetraTSDF: 3D Human Reconstruction from a Single Image with a Tetrahedral Outer Shell

Hayato Onizuka, Zehra Hayirci, DIego Thomas, Akihiro Sugimoto, Hideaki Uchiyama, Rin Ichiro Taniguchi

研究成果: Contribution to journalConference article査読

1 被引用数 (Scopus)


Recovering the 3D shape of a person from its 2D appearance is ill-posed due to ambiguities. Nevertheless, with the help of convolutional neural networks (CNN) and prior knowledge on the 3D human body, it is possible to overcome such ambiguities to recover detailed 3D shapes of human bodies from single images. Current solutions, however, fail to reconstruct all the details of a person wearing loose clothes. This is because of either (a) huge memory requirement that cannot be maintained even on modern GPUs or (b) the compact 3D representation that cannot encode all the details. In this paper, we propose the tetrahedral outer shell volumetric truncated signed distance function (TetraTSDF) model for the human body, and its corresponding part connection network (PCN) for 3D human body shape regression. Our proposed model is compact, dense, accurate, and yet well suited for CNN-based regression task. Our proposed PCN allows us to learn the distribution of the TSDF in the tetrahedral volume from a single image in an end-to-end manner. Results show that our proposed method allows to reconstruct detailed shapes of humans wearing loose clothes from single RGB images.

ジャーナルProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
出版ステータス出版済み - 2020
イベント2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 - Virtual, Online, 米国
継続期間: 6 14 20206 19 2020

All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • コンピュータ ビジョンおよびパターン認識


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