ObjBlur: A Curriculum Learning Approach With Progressive Object-Level Blurring for Improved Layout-to-Image Generation

摘要

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, based on progressive object-level blurring, effectively stabilizes training and enhances the quality of generated images. This strategy systematically applies varying degrees of blurring to individual objects or the background during training, starting with strong blurring to progressively cleaner images. Our findings reveal significant performance improvements, stabilized training, smoother convergence, and reduced variance between multiple runs. Moreover, our technique is compatible with generative adversarial networks and diffusion models, highlighting its versatility across generative modeling paradigms. We reach new state-of-the-art results on the complex COCO and Visual Genome datasets.