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Video-Based Self-supervised Human Depth Estimation

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Advances in Computer Graphics (CGI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14495))

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Abstract

In this paper, we propose a video-besed method for self-supervised human depth estimation, aiming at the problem of joint point distortion in human depth and insufficient utilization of 3D information in video-based depth estimation. We use the relative ordinal relations between human joint point pairs to deal with the problem of joint point distortion. Meanwhile, a temporal correlation module is proposed to focus on the temporal correlation between past and present frames, taking into account the influence of temporal characteristics in the video sequence. A hierarchical structure is adopted to fuse adjacent features, thus fully mine the 3D information based on the video. The experimental results show that this model significantly improves the human depth estimation performance, especially at the joints.

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Acknowledgements

The authors wish to acknowledge the financial support in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2020B1515120047, in part by Guangdong Natural Science Foundation under Grant 2021A1515011632 and 2021A1515012014.

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Authors and Affiliations

  1. School of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China

    Qianlin Li & Xiaoyan Zhang

Authors
  1. Qianlin Li
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  2. Xiaoyan Zhang
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Corresponding author

Correspondence to Xiaoyan Zhang .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  2. Shanghai Jiao Tong University, Shanghai, China

    Lei Bi

  3. University of Sydney, Sydney, NSW, Australia

    Jinman Kim

  4. MIRALab-CUI, University of Geneve, Carouge, Geneve, Switzerland

    Nadia Magnenat-Thalmann

  5. Swiss Federal Institute of Technology, Lausanne, Switzerland

    Daniel Thalmann

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Li, Q., Zhang, X. (2024). Video-Based Self-supervised Human Depth Estimation. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14495. Springer, Cham. https://doi.org/10.1007/978-3-031-50069-5_16

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  • DOI: https://doi.org/10.1007/978-3-031-50069-5_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50068-8

  • Online ISBN: 978-3-031-50069-5

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