Text-to-image generation has made remarkable progress with the emergence of diffusion models. However, it is still a difficult task to generate images for street views based on text, mainly because the road topology of street scenes is complex, the traffic status is diverse and the weather condition is various, which makes conventional text-to-image models difficult to deal with. To address these challenges, we propose a novel controllable text-to-image framework, named \textbf{Text2Street}. In the framework, we first introduce the lane-aware road topology generator, which achieves text-to-map generation with the accurate road structure and lane lines armed with the counting adapter, realizing the controllable road topology generation. Then, the position-based object layout generator is proposed to obtain text-to-layout generation through an object-level bounding box diffusion strategy, realizing the controllable traffic object layout generation. Finally, the multiple control image generator is designed to integrate the road topology, object layout and weather description to realize controllable street-view image generation. Extensive experiments show that the proposed approach achieves controllable street-view text-to-image generation and validates the effectiveness of the Text2Street framework for street views.

提出了一种名为Text2Street的新颖可控文本生成街景图像的框架，通过引入基于车道的道路拓扑生成器和基于位置的物体布局生成器实现可控的路网和交通物体布局生成，在集成道路拓扑、物体布局和天气描述的基础上实现可控的街景图像生成，并验证了Text2Street框架在街景生成方面的有效性。

Text2Street: 街景可控制的文本至图像生成