Trace Controlled Text to Image Generation

Text to Image generation is a fundamental and inevitable challenging task for visual linguistic modeling. The recent surge in this area such as DALL · E has shown breathtaking technical breakthroughs, however, it still lacks a precise control of the spatial relation corresponding to semantic text. To tackle this problem, mouse trace paired with text provides an interactive way, in which users can describe the imagined image with natural language while drawing traces to locate those they want. However, this brings the challenges of both controllability and compositionality of the generation. Motivated by this, we propose a Trace Controlled Text to Image Generation model (TCTIG), which takes trace as a bridge between semantic concepts and spatial conditions. Moreover, we propose a set of new technique to enhance the controllability and compositionality of generation, including trace guided re-weighting loss (TGR) and semantic aligned augmentation (SAA). In addition, we establish a solid benchmark for the trace-controlled text-to-image generation task, and introduce several new metrics to evaluate both the controllability and compositionality of the model. Upon that, we demonstrate TCTIG’s superior performance and further present the fruitful qualitative analysis of our model.