Papers › Multi-mapping Image-to-Image Translation via Learning Disentanglement

Multi-mapping Image-to-Image Translation via Learning Disentanglement

17 Sep 2019NeurIPS 2019 12arXiv:1909.07877archive 2025-07-28

Xiaoming Yu, Yuanqi Chen, Thomas Li, Shan Liu, Ge Li

Recent advances of image-to-image translation focus on learning the one-to-many mapping from two aspects: multi-modal translation and multi-domain translation. However, the existing methods only consider one of the two perspectives, which makes them unable to solve each other's problem. To address this issue, we propose a novel unified model, which bridges these two objectives. First, we disentangle the input images into the latent representations by an encoder-decoder architecture with a conditional adversarial training in the feature space. Then, we encourage the generator to learn multi-mappings by a random cross-domain translation. As a result, we can manipulate different parts of the latent representations to perform multi-modal and multi-domain translations simultaneously. Experiments demonstrate that our method outperforms state-of-the-art methods.

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