Disentangled Human Body Representation Based on Unsupervised Semantic-Aware Learning

TL;DR

This paper introduces an unsupervised learning framework for 3D human body representation that enables controllable, fine-grained semantic manipulation and precise reconstruction by disentangling geometric and semantic features.

Contribution

It proposes a novel skeleton-grouped disentangle strategy and residual learning scheme for unsupervised, controllable human body modeling with fine-grained semantics.

Findings

Achieves precise 3D human body reconstruction.

Enables controllable shape and pose manipulation.

Demonstrates effectiveness on public datasets.

Abstract

In recent years, more and more attention has been paid to the learning of 3D human representation. However, the complexity of lots of hand-defined human body constraints and the absence of supervision data limit that the existing works controllably and accurately represent the human body in views of semantics and representation ability. In this paper, we propose a human body representation with controllable fine-grained semantics and high precison of reconstruction in an unsupervised learning framework. In particularly, we design a whole-aware skeleton-grouped disentangle strategy to learn a correspondence between geometric semantical measurement of body and latent codes, which facilitates the control of shape and posture of human body by modifying latent coding paramerers. With the help of skeleton-grouped whole-aware encoder and unsupervised disentanglement losses, our representation model is learned by an unsupervised manner. Besides, a based-template residual learning scheme is injected into the encoder to ease of learning human body latent parameter in complicated body shape and pose spaces. Because of the geometrically meaningful latent codes, it can be used in a wide range of applications, from human body pose transfer to bilinear latent code interpolation. Further more, a part-aware decoder is utlized to promote the learning of controllable fine-grained semantics. The experimental results on public 3D human datasets show that the method has the ability of precise reconstruction.