  In this paper, we target image-based person-to-person virtual try-on in the
presence of diverse poses and large viewpoint variations. Existing methods are
restricted in this setting as they estimate garment warping flows mainly based
on 2D poses and appearance, which omits the geometric prior of the 3D human
body shape. Moreover, current garment warping methods are confined to localized
regions, which makes them ineffective in capturing long-range dependencies and
results in inferior flows with artifacts. To tackle these issues, we present
3D-aware global correspondences, which are reliable flows that jointly encode
global semantic correlations, local deformations, and geometric priors of 3D
human bodies. Particularly, given an image pair depicting the source and target
person, (a) we first obtain their pose-aware and high-level representations via
two encoders, and introduce a coarse-to-fine decoder with multiple refinement
modules to predict the pixel-wise global correspondence. (b) 3D parametric
human models inferred from images are incorporated as priors to regularize the
correspondence refinement process so that our flows can be 3D-aware and better
handle variations of pose and viewpoint. (c) Finally, an adversarial generator
takes the garment warped by the 3D-aware flow, and the image of the target
person as inputs, to synthesize the photo-realistic try-on result. Extensive
experiments on public benchmarks and our HardPose test set demonstrate the
superiority of our method against the SOTA try-on approaches.
