  We present ObjBlur, a novel curriculum learning approach to improve
layout-to-image generation models, where the task is to produce realistic
images from layouts composed of boxes and labels. Our method is based on
progressive object-level blurring, which effectively stabilizes training and
enhances the quality of generated images. This curriculum learning strategy
systematically applies varying degrees of blurring to individual objects or the
background during training, starting from strong blurring to progressively
cleaner images. Our findings reveal that this approach yields significant
performance improvements, stabilized training, smoother convergence, and
reduced variance between multiple runs. Moreover, our technique demonstrates
its versatility by being compatible with generative adversarial networks and
diffusion models, underlining its applicability across various generative
modeling paradigms. With ObjBlur, we reach new state-of-the-art results on the
complex COCO and Visual Genome datasets.
