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3 main points✔️ introduce new geometric constraints into the latent diffusion model training process to enhance perspective accuracy. ✔️ show that images of models trained using the constraint look more realistic 69.6% of the time than models trained without this constraint.✔️ We demonstrate that downstream tasks that benefit from more geometrically accurate input (e.g., monocular depth estimation) improve up to 7.03% in RMSE and 19.3% in SqRel. Enhancing Diffusion Models with 3D Perspective Geometry ConstraintswrittenbyRishi Upadhyay,Howard Zhang,Yunhao Ba,Ethan Yang,Blake Gella,Sicheng Jiang,Alex Wong,Achuta Kadambi(Submitted on 1 Dec 2023)Comments:Project Webpage:this http URLSubjects: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)code：The images used in this article are from the paper, the introductory slides, or were created based on them.
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