  Generative adversarial networks achieve great performance in photorealistic
image synthesis in various domains, including human images. However, they
usually employ latent vectors that encode the sampled outputs globally. This
does not allow convenient control of semantically-relevant individual parts of
the image, and is not able to draw samples that only differ in partial aspects,
such as clothing style. We address these limitations and present a generative
model for images of dressed humans offering control over pose, local body part
appearance and garment style. This is the first method to solve various aspects
of human image generation such as global appearance sampling, pose transfer,
parts and garment transfer, and parts sampling jointly in a unified framework.
As our model encodes part-based latent appearance vectors in a normalized
pose-independent space and warps them to different poses, it preserves body and
clothing appearance under varying posture. Experiments show that our flexible
and general generative method outperforms task-specific baselines for
pose-conditioned image generation, pose transfer and part sampling in terms of
realism and output resolution.
