  Recent successes in image synthesis are powered by large-scale diffusion
models. However, most methods are currently limited to either text- or
image-conditioned generation for synthesizing an entire image, texture transfer
or inserting objects into a user-specified region. In contrast, in this work we
focus on synthesizing complex interactions (ie, an articulated hand) with a
given object. Given an RGB image of an object, we aim to hallucinate plausible
images of a human hand interacting with it. We propose a two-step generative
approach: a LayoutNet that samples an articulation-agnostic
hand-object-interaction layout, and a ContentNet that synthesizes images of a
hand grasping the object given the predicted layout. Both are built on top of a
large-scale pretrained diffusion model to make use of its latent
representation. Compared to baselines, the proposed method is shown to
generalize better to novel objects and perform surprisingly well on
out-of-distribution in-the-wild scenes of portable-sized objects. The resulting
system allows us to predict descriptive affordance information, such as hand
articulation and approaching orientation. Project page:
https://judyye.github.io/affordiffusion-www
