Consistent 3D Hand Reconstruction in Video via Self-Supervised Learning

Zhigang Tu, Zhisheng Huang, Yujin Chen*, Di Kang, Linchao Bao, Bisheng Yang, Junsong Yuan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

We present a method for reconstructing accurate and consistent 3D hands from a monocular video. We observe that the detected 2D hand keypoints and the image texture provide important cues about the geometry and texture of the 3D hand, which can reduce or even eliminate the requirement on 3D hand annotation. Accordingly, in this work, we propose S2HAND, a self-supervised 3D hand reconstruction model, that can jointly estimate pose, shape, texture, and the camera viewpoint from a single RGB input through the supervision of easily accessible 2D detected keypoints. We leverage the continuous hand motion information contained in the unlabeled video data and explore S2HAND(V), which uses a set of weights shared S2HAND to process each frame and exploits additional motion, texture, and shape consistency constrains to obtain more accurate hand poses, and more consistent shapes and textures. Experiments on benchmark datasets demonstrate that our self-supervised method produces comparable hand reconstruction performance compared with the recent full-supervised methods in single-frame as input setup, and notably improves the reconstruction accuracy and consistency when using the video training data
Original languageEnglish
Article number10050006
Pages (from-to)9469-9485
Number of pages17
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume45
Issue number8
Early online date22 Feb 2023
DOIs
Publication statusPublished - Aug 2023

Keywords

  • Three-dimensional displays
  • Image reconstruction
  • Solid modeling
  • Shape
  • Training
  • Annotations
  • Video sequences

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