SIGGRAPH Asia 2021
Pose with Style: Detail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN
Badour AlBahar1,2 Jingwan Lu3 Jimei Yang3 Zhixin Shu3 Eli Shechtman3 Jia-Bin Huang1,4
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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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Bibtex
@article{albahar2021pose,
title = {Pose with {S}tyle: {D}etail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN},
author = {AlBahar, Badour and Lu, Jingwan and Yang, Jimei and Shu, Zhixin and Shechtman, Eli and Huang, Jia-Bin},
journal = {ACM Transactions on Graphics},
year = {2021}
}