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. The second stage then refines the initial and blurry result by training a U-Net-like generator in an adversarial way. Extensive experimental results on both 128×64 re-identification images and 256×256 fashion photos show that our model generates high-quality person images with convincing details. * Equal contribution.
For example, Ma et al. propose PG2 REF , which is a two-stage framework and uses the pose mask loss for generating person images based on an image of that person and human pose keypoints.
Pose Guided Person Image Generation
ArXiv
ArXiv
Computer Science