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arXiv · 1904.03379

Unsupervised Person Image Generation with Semantic Parsing Transformation

Sijie Song·Wei Zhang·Jiaying Liu·Tao Mei
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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, eg, 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.

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Sijie Song, Wei Zhang, Jiaying Liu, Tao Mei. 2019-04-06. Unsupervised Person Image Generation with Semantic Parsing Transformation. https://arxiv.org/abs/1904.03379

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