TCGIS: Text and Contour Guided Controllable Image Synthesis

Recently, text-to-image synthesis (T2I) has received extensive attention with encouraging results. However, the research still has the following challenges: 1) the quality of the synthesized images cannot be effectively guaranteed; 2) the human participation in the synthesis process is still insufficient. Facing the above challenges, we propose a text- and contour-guided artificial controllable image synthesis method. The method can synthesize corresponding image results based on the manual input text and simple contour, wherein the text determines the basic content and the contour determines the shape position. Based on the above idea, we propose a reasonable and efficient network structure using the attention mechanism and achieve amazing synthetic results. We validate the effectiveness of our proposed method on three widely used datasets, and both qualitative and quantitative experimental results show promising performance. In addition, we design a lightweight structure to further improve the practicability of the model.