G-HOP: Generative Hand-Object Prior for Interaction Reconstruction and
  Grasp Synthesis
Yufei Ye Abhinav Gupta Kris Kitani Shubham Tulsiani
We propose G-HOP, a denoising diffusion based generative prior for
hand-object interactions that allows modeling both the 3D object and a human
hand, conditioned on the object category. To learn a 3D spatial diffusion model
that can capture this joint distribution, we represent the human hand via a
skeletal distance field to obtain a representation aligned with the (latent)
signed distance field for the object. We show that this hand-object prior can
then serve as generic guidance to facilitate other tasks like reconstruction
from interaction clip and human grasp synthesis. We believe that our model,
trained by aggregating seven diverse real-world interaction datasets spanning
across 155 categories, represents a first approach that allows jointly
generating both hand and object. Our empirical evaluations demonstrate the
benefit of this joint prior in video-based reconstruction and human grasp
synthesis, outperforming current task-specific baselines.
  Project website: https://judyye.github.io/ghop-www