  Generation of high-quality person images is challenging, due to the
sophisticated entanglements among image factors, e.g., appearance, pose,
foreground, background, local details, global structures, etc. In this paper,
we present a novel end-to-end framework to generate realistic person images
based on given person poses and appearances. The core of our framework is a
novel generator called Appearance-aware Pose Stylizer (APS) which generates
human images by coupling the target pose with the conditioned person appearance
progressively. The framework is highly flexible and controllable by effectively
decoupling various complex person image factors in the encoding phase, followed
by re-coupling them in the decoding phase. In addition, we present a new
normalization method named adaptive patch normalization, which enables
region-specific normalization and shows a good performance when adopted in
person image generation model. Experiments on two benchmark datasets show that
our method is capable of generating visually appealing and realistic-looking
results using arbitrary image and pose inputs.
