  Modern diffusion-based image generative models have made significant progress
and become promising to enrich training data for the object detection task.
However, the generation quality and the controllability for complex scenes
containing multi-class objects and dense objects with occlusions remain
limited. This paper presents ODGEN, a novel method to generate high-quality
images conditioned on bounding boxes, thereby facilitating data synthesis for
object detection. Given a domain-specific object detection dataset, we first
fine-tune a pre-trained diffusion model on both cropped foreground objects and
entire images to fit target distributions. Then we propose to control the
diffusion model using synthesized visual prompts with spatial constraints and
object-wise textual descriptions. ODGEN exhibits robustness in handling complex
scenes and specific domains. Further, we design a dataset synthesis pipeline to
evaluate ODGEN on 7 domain-specific benchmarks to demonstrate its
effectiveness. Adding training data generated by ODGEN improves up to 25.3%
mAP@.50:.95 with object detectors like YOLOv5 and YOLOv7, outperforming prior
controllable generative methods. In addition, we design an evaluation protocol
based on COCO-2014 to validate ODGEN in general domains and observe an
advantage up to 5.6% in mAP@.50:.95 against existing methods.
