  Diffusion models have attracted significant attention due to the remarkable
ability to create content and generate data for tasks like image
classification. However, the usage of diffusion models to generate the
high-quality object detection data remains an underexplored area, where not
only image-level perceptual quality but also geometric conditions such as
bounding boxes and camera views are essential. Previous studies have utilized
either copy-paste synthesis or layout-to-image (L2I) generation with
specifically designed modules to encode the semantic layouts. In this paper, we
propose the GeoDiffusion, a simple framework that can flexibly translate
various geometric conditions into text prompts and empower pre-trained
text-to-image (T2I) diffusion models for high-quality detection data
generation. Unlike previous L2I methods, our GeoDiffusion is able to encode not
only the bounding boxes but also extra geometric conditions such as camera
views in self-driving scenes. Extensive experiments demonstrate GeoDiffusion
outperforms previous L2I methods while maintaining 4x training time faster. To
the best of our knowledge, this is the first work to adopt diffusion models for
layout-to-image generation with geometric conditions and demonstrate that
L2I-generated images can be beneficial for improving the performance of object
detectors.
