CroGen: Probing Spatial Control at Extreme Target Densities in Image Generation

Advanced image diffusion models generate realistic images and support flexible spatial control. However, we find that spatial correspondence deteriorates as the number of instances we aim to control increases, despite visually convincing outputs. We term this phenomenon spatial constraint overload. In this paper, we explore this phenomenon using dense crowds as a case study and introduce CroGen, a simple and elegant method to alleviate it. Specifically, CroGen combines Proximity Field Conditioning (PFC) and Pixel-Packing Injection (PPI). Our PFC converts target locations into a quantized distance field, providing dense guidance while keeping individual locations explicit. Our PPI retains fine spatial details through pixel packing and injects the packed condition through a single linear projection. We further introduce a dataset of 71K crowd images spanning diverse crowd densities, with point annotations and text prompts. Comprehensive experiments on controllable generation and downstream dense crowd counting show that our CroGen improves spatial consistency without sacrificing image quality. In particular, our method better maintains this consistency as the number of controlled instances grows, demonstrating its effectiveness in alleviating spatial constraint overload. The dataset and model will be released.