Various works have been extensively studied in the research of text-to-image
generation. Although existing models perform well in text-to-image generation,
there are significant challenges when directly employing them to generate
images in dialogs. In this paper, we first highlight a new problem:
dialog-to-image generation, that is, given the dialog context, the model should
generate a realistic image which is consistent with the specified conversation
as response. To tackle the problem, we propose an efficient approach for
dialog-to-image generation without any intermediate translation, which
maximizes the extraction of the semantic information contained in the dialog.
Considering the characteristics of dialog structure, we put segment token
before each sentence in a turn of a dialog to differentiate different speakers.
Then, we fine-tune pre-trained text-to-image models to enable them to generate
images conditioning on processed dialog context. After fine-tuning, our
approach can consistently improve the performance of various models across
multiple metrics. Experimental results on public benchmark demonstrate the
effectiveness and practicability of our method.

通过语料上进行的实验结果证明，我们的方法有效且实用，能够生成与指定对话一致的逼真图像，并在多个度量指标上持续提高各种模型的性能。