TL;DR
This paper introduces a user-friendly 3D human body reshaping system that uses limited anthropometric measurements and a novel local mapping technique to generate accurate 3D models efficiently.
Contribution
It proposes a new feature-selection-based local mapping approach that simplifies anthropometric input requirements for realistic 3D body reshaping.
Findings
Achieves lower mean reconstruction error than existing methods
Uses only 3-5 anthropometric measurements for modeling
Receives positive feedback in user study with 68 volunteers
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…
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Taxonomy
MethodsLinear Regression
