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

Understand

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.