Image semantic transformation aims to convert one image into another image with different semantic features (e.g., face pose, hairstyle). The previous methods, which learn the mapping function from one image domain to the other, require supervised information directly or indirectly. In this paper, we propose an unsupervised image semantic transformation method called semantic transformation generative adversarial networks (ST-GAN), and experimentally verify it on face dataset. We further improve ST-GAN with the Wasserstein distance to generate more realistic images and propose a method called local mutual information maximization to obtain a more explicit semantic transformation. ST-GAN has the ability to map the image semantic features into the latent vector and then perform transformation by controlling the latent vector.
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