Diffusion Handles Enabling 3D Edits for Diffusion Models by Lifting Activations to 3D

Karran Pandey, Paul Guerrero, Matheus Gadelha, Yannick Hold-Geoffroy, Karan Singh, Niloy J. Mitra

Abstract: Diffusion Handles is a novel approach to enable 3D object edits on diffusion images, requiring only existing pre-trained diffusion models depth estimation, without any fine-tuning or 3D object retrieval. The edited results remain plausible, photo-real, and preserve object identity. Diffusion Handles address a critically missing facet of generative image-based creative design. Our key insight is to lift diffusion activations for a selected object to 3D using a proxy depth, 3D-transform the depth and associated activations, and project them back to image space. The diffusion process guided by the manipulated activations produces plausible edited images showing complex 3D occlusion and lighting effects. We evaluate Diffusion Handles: quantitatively, on a large synthetic data benchmark; and qualitatively by a user study, showing our output to be more plausible, and better than prior art at both, 3D editing and identity control.