3D Human Body Reshaping with Anthropometric Modeling

• URL: http://arxiv.org/abs/2104.01762v1
• Date: Mon, 5 Apr 2021 04:09:39 GMT
• Title: 3D Human Body Reshaping with Anthropometric Modeling
• Authors: Yanhong Zeng, Jianlong Fu, Hongyang Chao
• Abstract summary: Reshaping accurate and realistic 3D human bodies from anthropometric parameters poses a fundamental challenge for person identification, online shopping and virtual reality. Existing approaches for creating such 3D shapes often suffer from complex measurement by range cameras or high-end scanners. This paper proposes a novel feature-selection-based local mapping technique, which enables automatic anthropometric parameter modeling for each body facet.
• Score: 59.51820187982793
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Reshaping accurate and realistic 3D human bodies from anthropometric parameters (e.g., height, chest size, etc.) poses a fundamental challenge for person identification, online shopping and virtual reality. Existing approaches for creating such 3D shapes often suffer from complex measurement by range cameras or high-end scanners, which either involve heavy expense cost or result in low quality. However, these high-quality equipments limit existing approaches in real applications, because the equipments are not easily accessible for common users. In this paper, we have designed a 3D human body reshaping system by proposing a novel feature-selection-based local mapping technique, which enables automatic anthropometric parameter modeling for each body facet. Note that the proposed approach can leverage limited anthropometric parameters (i.e., 3-5 measurements) as input, which avoids complex measurement, and thus better user-friendly experience can be achieved in real scenarios. Specifically, the proposed reshaping model consists of three steps. First, we calculate full-body anthropometric parameters from limited user inputs by imputation technique, and thus essential anthropometric parameters for 3D body reshaping can be obtained. Second, we select the most relevant anthropometric parameters for each facet by adopting relevance masks, which are learned offline by the proposed local mapping technique. Third, we generate the 3D body meshes by mapping matrices, which are learned by linear regression from the selected parameters to mesh-based body representation. We conduct experiments by anthropomorphic evaluation and a user study from 68 volunteers. Experiments show the superior results of the proposed system in terms of mean reconstruction error against the state-of-the-art approaches.

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