Colorization of Logo Sketch Based on Conditional Generative Adversarial Networks

Logo design is a complex process for designers and color plays a very important role in logo design. The automatic colorization of logo sketch is of great value and full of challenges. In this paper, we propose a new logo design method based on Conditional Generative Adversarial Networks, which can output multiple colorful logos only by providing one logo sketch. We improve the traditional U-Net structure, adding channel attention and spatial attention in the process of skip-connection. In addition, the generator consists of parallel attention-based U-Net blocks, which can output multiple logo images. During the model optimization process, a style loss function is proposed to improve the color diversity of the logos. We evaluate our method on the self-built edges2logos dataset and the public edges2shoes dataset. Experimental results show that our method can generate more colorful and realistic logo images based on simple sketches. Compared to the classic networks, the logos generated by our network are also superior in visual effects.