2017/05/25 by Liqian Ma, Xu Jia, Ma, Liqian +9 · 31 citations
Computer Science · #Advanced Image Processing Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Human Pose and Action Recognition #cs.CV
paper · pdf · doi:10.48550/arxiv.1705.09368
Xu Jia and Qianru Sun contribute equally. Accepted in Proceedings of 31st Conference on Neural Information Processing Systems (NIPS 2017)
openalex publication_date 2017/05/25 · arxiv created 2018/01/28 · arxiv updated 2018/01/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper proposes the novel Pose Guided Person Generation Network (PG2) that allows to synthesize person images in arbitrary poses, based on an image of that person and a novel pose. Our generation framework PG2 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.