Customizable GAN: Customizable Image Synthesis Based on Adversarial Learning

In this paper, we propose a highly flexible and controllable image synthesis method based on the simple contour and text description. The contour determines the object’s basic shape, and the text describes the specific content of the object. The method is verified in the Caltech-UCSD Birds (CUB) and Oxford-102 flower datasets. The experimental results demonstrate its effectiveness and superiority. Simultaneously, our method can synthesize the high-quality image synthesis results based on artificial hand-drawing contour and text description, which demonstrates the high flexibility and customizability of our method further.