Pose with Style: Detail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN

• URL: http://arxiv.org/abs/2109.06166v1
• Date: Mon, 13 Sep 2021 17:59:33 GMT
• Title: Pose with Style: Detail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN
• Authors: Badour AlBahar, Jingwan Lu, Jimei Yang, Zhixin Shu, Eli Shechtman, Jia-Bin Huang
• Abstract summary: We present an algorithm for re-rendering a person from a single image under arbitrary poses. Existing methods often have difficulties in hallucinating occluded contents photo-realistically while preserving the identity and fine details in the source image. We show that our method compares favorably against the state-of-the-art algorithms in both quantitative evaluation and visual comparison.
• Score: 88.62422914645066
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present an algorithm for re-rendering a person from a single image under arbitrary poses. Existing methods often have difficulties in hallucinating occluded contents photo-realistically while preserving the identity and fine details in the source image. We first learn to inpaint the correspondence field between the body surface texture and the source image with a human body symmetry prior. The inpainted correspondence field allows us to transfer/warp local features extracted from the source to the target view even under large pose changes. Directly mapping the warped local features to an RGB image using a simple CNN decoder often leads to visible artifacts. Thus, we extend the StyleGAN generator so that it takes pose as input (for controlling poses) and introduces a spatially varying modulation for the latent space using the warped local features (for controlling appearances). We show that our method compares favorably against the state-of-the-art algorithms in both quantitative evaluation and visual comparison.

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