An Embeddable Implicit IUVD Representation for Part-based 3D Human
  Surface Reconstruction
Baoxing Li Yong Deng Yehui Yang Xu Zhao
To reconstruct a 3D human surface from a single image, it is important to
consider human pose, shape and clothing details simultaneously. In recent
years, a combination of parametric body models (such as SMPL) that capture body
pose and shape prior, and neural implicit functions that learn flexible
clothing details, has been used to integrate the advantages of both approaches.
However, the combined representation introduces additional computation, e.g.
signed distance calculation, in 3D body feature extraction, which exacerbates
the redundancy of the implicit query-and-infer process and fails to preserve
the underlying body shape prior. To address these issues, we propose a novel
IUVD-Feedback representation, which consists of an IUVD occupancy function and
a feedback query algorithm. With this representation, the time-consuming signed
distance calculation is replaced by a simple linear transformation in the IUVD
space, leveraging the SMPL UV maps. Additionally, the redundant query points in
the query-and-infer process are reduced through a feedback mechanism. This
leads to more reasonable 3D body features and more effective query points,
successfully preserving the parametric body prior. Moreover, the IUVD-Feedback
representation can be embedded into any existing implicit human reconstruction
pipelines without modifying the trained neural networks. Experiments on
THuman2.0 dataset demonstrate that the proposed IUVD-Feedback representation
improves result robustness and achieves three times faster acceleration in the
query-and-infer process. Furthermore, this representation has the potential to
be used in generative applications by leveraging its inherited semantic
information from the parametric body model.