G-HOP: Generative Hand-Object Prior for Interaction Reconstruction and Grasp Synthesis

March 29, 2024


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 several 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.

Download the Paper


Written by

Judy Ye

Abhinav Gupta

Kris Kitani

Shubham Tulsiani



Research Topics

Computer Vision

Related Publications

May 06, 2024



Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint

Haoyue Tang, Tian Xie

May 06, 2024

April 23, 2024


Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidar

Jamie Tolan, Eric Yang, Ben Nosarzewski, Guillaume Couairon, Huy Vo, John Brandt, Justine Spore, Sayantan Majumdar, Daniel Haziza, Janaki Vamaraju, Theo Moutakanni, Piotr Bojanowski, Tracy Johns, Brian White, Tobias Tiecke, Camille Couprie, Edward Saenz

April 23, 2024

April 23, 2024



Generating Illustrated Instructions

Sachit Menon, Ishan Misra, Rohit Girdhar

April 23, 2024

April 18, 2024


Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation

Jonas Kohler, Albert Pumarola, Edgar Schoenfeld, Artsiom Sanakoyeu, Roshan Sumbaly, Peter Vajda, Ali Thabet

April 18, 2024

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.