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

InterControl: Generate Human Motion Interactions by Controlling Every Joint