UCLA

Generative diffusion models have recently become extremely popular in a variety of domains,but especially in image generation. These models are capable of generating a wide variety of high-quality images and can be guided by text prompts, depth maps, and more. Despite these impressive capabilities, these models typically generate images with poor projective geometry. As a result, generated images differ significantly from real images, decreasing the photo-realism of generated images. In addition, since perspective is crucial for representing 3D information in 2D images, discrepancies in projective geometry limit the use of generative models as synthetic data generators. In this work, we introduce a geometric constraint to improve the projective geometry of diffusion models and show that outputs of models trained with this constraint both appear more photo-realistic and serve as useful synthetic data by improving the performance of downstream models fine-tuned on generated images.