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

Q4 Engineering
A.V. Kugaevskikh, M.A. Bolshim, I.F. Sattarov
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引用次数: 0

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.
使用人体测量点来引入限制,以合成使用SMPL的人体三维模型
生成一个真实的人体三维模型是一项非常耗时的任务。即使有必要的计算资源,与平均体格不同的人的身材也会出现生成错误。本文提出了一种仅从两张全脸和侧面照片中读取人体测量数据的实验算法。提出的利用人体测量点选择生成问题的解决方案涉及设置SMPL(蒙皮多人线性模型)模型的约束。对于基于实证研究的人体分割,使用了基于COCO segmentation 2017数据集训练的全连接卷积神经网络(FCN) ResNet101的改进。在此基础上,为全脸和侧面照片的人体测量点检测提供了依据。在确定人体测量点的误差范围从2%到5%取决于他们的位置。使用Levenberg- Marquardt算法计算SMPL渲染模型的约束。为了使其正确运行,考虑到该任务的特点,提出了一个特殊的代价函数。本文作者收集的数据集(117个不同体型和身高的人)表明,所提出的方法可以获得较小的平均绝对误差(MAE = 0.0395 m)和较高的决定系数(R2 = 0.913)。人体测量点图形为生成图形设定了更严格的条件,任何与图形的偏差都是大生成误差的结果。提出的解决方案允许您精确地生成人体模型。同时,对计算资源的低要求和用户初始照片的质量仍然存在。所提出的解决方案可以用于在线试衣间,这增加了任务的复杂性,因为只需要从两张图片中恢复人物,并且需要准确地再现男性和女性人物的特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
0.70
自引率
0.00%
发文量
102
审稿时长
8 weeks
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