  Body reshaping is an important procedure in portrait photo retouching. Due to
the complicated structure and multifarious appearance of human bodies, existing
methods either fall back on the 3D domain via body morphable model or resort to
keypoint-based image deformation, leading to inefficiency and unsatisfied
visual quality. In this paper, we address these limitations by formulating an
end-to-end flow generation architecture under the guidance of body structural
priors, including skeletons and Part Affinity Fields, and achieve
unprecedentedly controllable performance under arbitrary poses and garments. A
compositional attention mechanism is introduced for capturing both visual
perceptual correlations and structural associations of the human body to
reinforce the manipulation consistency among related parts. For a comprehensive
evaluation, we construct the first large-scale body reshaping dataset, namely
BR-5K, which contains 5,000 portrait photos as well as professionally retouched
targets. Extensive experiments demonstrate that our approach significantly
outperforms existing state-of-the-art methods in terms of visual performance,
controllability, and efficiency. The dataset is available at our website:
https://github.com/JianqiangRen/FlowBasedBodyReshaping.
