Text-conditioned human motion generation model has achieved great progress by
introducing diffusion models and corresponding control signals. However, the
interaction between humans are still under explored. To model interactions of
arbitrary number of humans, we define interactions as human joint pairs that
are either in contact or separated, and leverage {\em Large Language Model
(LLM) Planner} to translate interaction descriptions into contact plans. Based
on the contact plans, interaction generation could be achieved by spatially
controllable motion generation methods by taking joint contacts as spatial
conditions. We present a novel approach named InterControl for flexible spatial
control of every joint in every person at any time by leveraging motion
diffusion model only trained on single-person data. We incorporate a motion
controlnet to generate coherent and realistic motions given sparse spatial
control signals and a loss guidance module to precisely align any joint to the
desired position in a classifier guidance manner via Inverse Kinematics (IK).
Extensive experiments on HumanML3D and KIT-ML dataset demonstrate its
effectiveness in versatile joint control. We also collect data of joint contact
pairs by LLMs to show InterControl's ability in human interaction generation.

模型 InterControl 利用大型语言模型规划器将人的交互描述转化为接触计划，通过基于接触计划的空间可控运动生成方法实现交互生成，进而实现对每个人的每个关节的灵活空间控制。