Hierarchical Generation of Human-Object Interactions with Diffusion
  Probabilistic Models
Huaijin Pi Sida Peng Minghui Yang Xiaowei Zhou Hujun Bao
This paper presents a novel approach to generating the 3D motion of a human
interacting with a target object, with a focus on solving the challenge of
synthesizing long-range and diverse motions, which could not be fulfilled by
existing auto-regressive models or path planning-based methods. We propose a
hierarchical generation framework to solve this challenge. Specifically, our
framework first generates a set of milestones and then synthesizes the motion
along them. Therefore, the long-range motion generation could be reduced to
synthesizing several short motion sequences guided by milestones. The
experiments on the NSM, COUCH, and SAMP datasets show that our approach
outperforms previous methods by a large margin in both quality and diversity.
The source code is available on our project page
https://zju3dv.github.io/hghoi.