  Diffusion Handles is a novel approach to enabling 3D object edits on
diffusion images. We accomplish these edits using existing pre-trained
diffusion models, and 2D image 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, and significantly advance the
state-of-the-art in generative image editing. Our key insight is to lift
diffusion activations for an object to 3D using a proxy depth, 3D-transform the
depth and associated activations, and project them back to image space. The
diffusion process applied to the manipulated activations with identity control,
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. Project Webpage: https://diffusionhandles.github.io/
