Unsupervised Person Image Generation With Semantic Parsing Transformation

Sijie Song, Wei Zhang, Jiaying Liu, Tao Mei; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 2357-2366

Abstract


In this paper, we address unsupervised pose-guided person image generation, which is known challenging due to non-rigid deformation. Unlike previous methods learning a rock-hard direct mapping between human bodies, we propose a new pathway to decompose the hard mapping into two more accessible subtasks, namely, semantic parsing transformation and appearance generation. Firstly, a semantic generative network is proposed to transform between semantic parsing maps, in order to simplify the non-rigid deformation learning. Secondly, an appearance generative network learns to synthesize semantic-aware textures. Thirdly, we demonstrate that training our framework in an end-to-end manner further refines the semantic maps and final results accordingly. Our method is generalizable to other semantic-aware person image generation tasks, e.g., clothing texture transfer and controlled image manipulation. Experimental results demonstrate the superiority of our method on DeepFashion and Market-1501 datasets, especially in keeping the clothing attributes and better body shapes.

Related Material


[pdf] [supp]
[bibtex]
@InProceedings{Song_2019_CVPR,
author = {Song, Sijie and Zhang, Wei and Liu, Jiaying and Mei, Tao},
title = {Unsupervised Person Image Generation With Semantic Parsing Transformation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}