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

Autor:	AlBahar, Badour, Lu, Jingwan, Yang, Jimei, Shu, Zhixin, Shechtman, Eli, Huang, Jia-Bin
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: SIGGRAPH Asia 2021. Project page: https://pose-with-style.github.io/
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2109.06166 Zobrazit plný text záznamu View this record from Arxiv