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An iterative 3D human body reconstruction method driven by personalized dimensional prior knowledge

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Abstract

In recent years, remarkable strides have been made in 3D human reconstruction. However, the focus has primarily been on pose reconstruction, which fails to capture the true shape of the human body, particularly for individuals who are overweight or underweight. Therefore, based on a combination of regression and optimization, as well as ensuring accurate posture, we incorporate reasonable prior knowledge for the shape part of the reconstructions during iteration to obtain a Skinned Multi-Person Linear Model (SMPL) with a reasonable shape. During the training process, in regard to the pose, we fit 2D joint points, which can be obtained through images, and the training is still feasible even if the ground truth is missing; for the shape, we obtain the dimension information by measurement, but the initial height and waist need to be provided. To assess our method, we collect the dimension data from 200 individuals and achieved a remarkable average precision of 97\(\%\) based on a thorough comparison of the results. Our main research objective is to construct a more realistic model of the human body by adding reasonable body dimension information as prior knowledge during the iterative loop, and to measure 38 body dimensions of the human body. Our experiments demonstrate that our method, in regard to the shape, outperforms other state-of-the-art techniques while simultaneously maintaining postural accuracy.

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The data availability statement

The “Human3.6M” data that support the findings of this study are available in Human3.6M, the “3DPW” are available in 3DPW, the “MPI-INF-3DHP” are available in MPI-INF-3DHP, the “COCO” are available in COCO, the “LSP” are available in LSP, the “MPII” are available in MPII, and the “SSP-3D” are available in SSP-3D

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Acknowledgements

This work was supported by the Shandong Provincial Natural Science Foundation (ZR2021MF058).

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Authors and Affiliations

  1. Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum(East China), Qingdao, 266580, People’s Republic of China

    Yukun Dong, Qi Yuan, Rongrong Peng, Shengtao Wang & Junqi Sun

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  1. Yukun Dong
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  2. Qi Yuan
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  3. Rongrong Peng
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Correspondence to Yukun Dong.

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Dong, Y., Yuan, Q., Peng, R. et al. An iterative 3D human body reconstruction method driven by personalized dimensional prior knowledge. Appl Intell 54, 738–748 (2024). https://doi.org/10.1007/s10489-023-05214-y

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