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	The use of anthropometric points to introduce restrictions into the synthesis of a 3D model of the human body using SMPL</div>

Kugaevskikh Alexander V., Bolshim Maksim A., Sattarov Ildar F.

2023 , VOLUME 23, NUMBER 5 ( september-october )

ISSN 2226-1494 (print), ISSN 2500-0373 (online)

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Nikiforov
Vladimir O.
D.Sc., Prof.

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doi: 10.17586/2226-1494-2023-23-5-935-945

The use of anthropometric points to introduce restrictions into the synthesis of a 3D model of the human body using SMPL

A. V. Kugaevskikh, M. A. Bolshim, I. F. Sattarov

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Article in Russian

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Kugaevskikh A.V., Bolshim M.A., Sattarov I.F. The use of anthropometric points to introduce restrictions into the synthesis of a 3D model of the human body using SMPL. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2023, vol. 23, no. 5, pp. 935–945 (in Russian). doi: 10.17586/2226-1494-2023-23-5-935-945

Abstract

Generating a realistic three-dimensional model of the human body is a very time-consuming task. Even with the necessary computing resources, generation errors occur on the figures of people who differ from the average physique. In this paper, an experimental algorithm for reading anthropometric data from only two full-face and profile photographs is proposed. The proposed solution to the problem of generation using the selection of anthropometric points involves setting the constraints of the SMPL (Skinned Multi-Person Linear Model) model. For segmentation of the human body based on empirical studies, a modification of the Fully Connected Convolutional Neural Network (FCN) ResNet101, trained on the COCO Segmentation 2017 dataset, was used. With its help, the basis for the detection of anthropometric points in full-face and profile photos was obtained. The error in determining anthropometric points ranges from 2 to 5 % depending on their location. The constraints for the SMPL rendering model are calculated using the Levenberg- Marquardt algorithm. For its correct operation, a special cost function is proposed, taking into account the features of this task. The dataset collected by the authors of the article (117 people of different physiques and height) shows that the proposed method allows you to obtain a small average absolute error (MAE = 0.0395 m) and a high coefficient of determination (R² = 0.913). The graph of anthropometric points sets stricter conditions for generating a figure and any deviation from the graph is a consequence of a large generation error. The proposed solution allows you to accurately generate a model of the human body. At the same time, low requirements for computing resources and the quality of users’ initial photos remain. The proposed solution can be used in online fitting rooms, which adds additional complexity to the task due to the requirements to restore the figure from only two pictures as well as the need to accurately reproduce the features of male and female figures.

Keywords: generation of human body model, SMPL, anthropometric points, keypoint recognition, 3d mesh model

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