  We present a new deep learning approach to pose-guided resynthesis of human
photographs. At the heart of the new approach is the estimation of the complete
body surface texture based on a single photograph. Since the input photograph
always observes only a part of the surface, we suggest a new inpainting method
that completes the texture of the human body. Rather than working directly with
colors of texture elements, the inpainting network estimates an appropriate
source location in the input image for each element of the body surface. This
correspondence field between the input image and the texture is then further
warped into the target image coordinate frame based on the desired pose,
effectively establishing the correspondence between the source and the target
view even when the pose change is drastic. The final convolutional network then
uses the established correspondence and all other available information to
synthesize the output image. A fully-convolutional architecture with deformable
skip connections guided by the estimated correspondence field is used. We show
state-of-the-art result for pose-guided image synthesis. Additionally, we
demonstrate the performance of our system for garment transfer and pose-guided
face resynthesis.
