  We present an algorithm for re-rendering a person from a single image under
arbitrary poses. Existing methods often have difficulties in hallucinating
occluded contents photo-realistically while preserving the identity and fine
details in the source image. We first learn to inpaint the correspondence field
between the body surface texture and the source image with a human body
symmetry prior. The inpainted correspondence field allows us to transfer/warp
local features extracted from the source to the target view even under large
pose changes. Directly mapping the warped local features to an RGB image using
a simple CNN decoder often leads to visible artifacts. Thus, we extend the
StyleGAN generator so that it takes pose as input (for controlling poses) and
introduces a spatially varying modulation for the latent space using the warped
local features (for controlling appearances). We show that our method compares
favorably against the state-of-the-art algorithms in both quantitative
evaluation and visual comparison.
