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Pose Guided Person Image Generation

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons203020

Sun,  Qianru
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45383

Schiele,  Bernt
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Volltexte (frei zugänglich)

arXiv:1705.09368.pdf
(Preprint), 3MB

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Zitation

Ma, L., Jia, X., Sun, Q., Schiele, B., Tuytelaars, T., & Van Gool, L. (2017). Pose Guided Person Image Generation. Retrieved from http://arxiv.org/abs/1705.09368.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-002D-7CB9-8
Zusammenfassung
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$\times$64 re-identification images and 256$\times$256 fashion photos show that our model generates high-quality person images with convincing details.