The paper proposes a human image generator conditioned on appearance and human pose. The proposed generation is based on adversarial training architecture where two-step generative networks that produces high resolution image to feed into a discriminator. In the generator part, the first generator produce a coarse image using a U-shape network given appearance and pose map, then the second generator takes the coarse input with the original appearance to predict residual to refine the coarse image. The paper proposes a few important ideas. Conditioned on appearance and pose information, the proposed generator stacks two networks to adopt a coarse-to-fine strategy.

This paper proposes the novel Pose Guided Person Generation Network (PG$ 2$) that allows to synthesize person images in arbitrary poses, based on an image of that person and a novel pose. Our generation framework PG$ 2$ utilizes the pose information explicitly and consists of two key stages: pose integration and image refinement. In the first stage the condition image and the target pose are fed into a U-Net-like network to generate an initial but coarse image of the person with the target pose. Extensive experimental results on both 128$\times$64 re-identification images and 256$\times$256 fashion photos show that our model generates high-quality person images with convincing details. Papers published at the Neural Information Processing Systems Conference.