  Given a source portrait, the automatic human body reshaping task aims at
editing it to an aesthetic body shape. As the technology has been widely used
in media, several methods have been proposed mainly focusing on generating
optical flow to warp the body shape. However, those previous works only
consider the local transformation of different body parts (arms, torso, and
legs), ignoring the global affinity, and limiting the capacity to ensure
consistency and quality across the entire body. In this paper, we propose a
novel Adaptive Affinity-Graph Network (AAGN), which extracts the global
affinity between different body parts to enhance the quality of the generated
optical flow. Specifically, our AAGN primarily introduces the following
designs: (1) we propose an Adaptive Affinity-Graph (AAG) Block that leverages
the characteristic of a fully connected graph. AAG represents different body
parts as nodes in an adaptive fully connected graph and captures all the
affinities between nodes to obtain a global affinity map. The design could
better improve the consistency between body parts. (2) Besides, for
high-frequency details are crucial for photo aesthetics, a Body Shape
Discriminator (BSD) is designed to extract information from both high-frequency
and spatial domain. Particularly, an SRM filter is utilized to extract
high-frequency details, which are combined with spatial features as input to
the BSD. With this design, BSD guides the Flow Generator (FG) to pay attention
to various fine details rather than rigid pixel-level fitting. Extensive
experiments conducted on the BR-5K dataset demonstrate that our framework
significantly enhances the aesthetic appeal of reshaped photos, marginally
surpassing all previous work to achieve state-of-the-art in all evaluation
metrics.
