  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.
