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Generating Multiple Hypotheses for 3D Human Mesh and Pose Using Conditional Generative Adversarial Nets

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Computer Vision – ACCV 2022 (ACCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13844))

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Abstract

Despite recent successes in 3D human mesh/pose recovery, the human mesh/pose reconstruction ambiguity is a challenging problem that can not be avoided as lighting, occlusion or self-occlusion in scenes happens. We argue that there could be multiple 3D human meshes corresponding a single image from a view point, because we really do not know what happens in extreme lighting or behind occlusion/self occlusion. In this paper, we address the problem using Conditional Generative Adversarial Nets (CGANs) to generate multiple hypotheses for 3D human mesh and pose from a single image under the condition of 2D joints and relative depth of adjacent joints. The initial estimation of 2D human skeletons, relative depth and features is taken as input of CGANs to train the generator and discriminator in the first stage. Then the generator of CGANs is used to generate multiple human meshes via different conditions which are consistent with human silhouette and 2D joint points in the second stage. Selecting and clustering are utilized to eliminate abnormal and redundant human meshes. The number of hypothesis is not unified for each single image, and it is dependent on 2D pose ambiguity. Unlike the existing end-to-end 3D human mesh recovery methods, our approach consists of three task-specific deep networks trained separately to mitigate the training burden in terms of time and datasets. Our approach has been evaluated not only on the datasets of laboratory and real scenes but also on Internet images qualitatively and quantitatively, and experimental results demonstrate the effectiveness of our approach.

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Acknowledgement

This work is supported by the National Natural Science Foundation of China (NSFC No. 61971106). Authors would like to thank all reviewers for their valuable and meticulous comments.

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Authors and Affiliations

  1. School of Automation Engineering, University of Electronic and Scientific Technology of China, Chengdu, China

    Xu Zheng, Yali Zheng & Shubing Yang

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  1. Xu Zheng
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  2. Yali Zheng
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  3. Shubing Yang
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Corresponding author

Correspondence to Yali Zheng .

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Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

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Zheng, X., Zheng, Y., Yang, S. (2023). Generating Multiple Hypotheses for 3D Human Mesh and Pose Using Conditional Generative Adversarial Nets. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13844. Springer, Cham. https://doi.org/10.1007/978-3-031-26316-3_13

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  • DOI: https://doi.org/10.1007/978-3-031-26316-3_13

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