Papers › Pose Guided Person Image Generation

Pose Guided Person Image Generation

25 May 2017NeurIPS 2017 12arXiv:1705.09368archive 2025-07-28

Liqian Ma, Xu Jia, Qianru Sun, Bernt Schiele, Tinne Tuytelaars, Luc van Gool

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.

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