Controllable Human-Object Interaction Synthesis
Jiaman Li Alexander Clegg Roozbeh Mottaghi Jiajun Wu Xavier Puig C. Karen Liu
Synthesizing semantic-aware, long-horizon, human-object interaction is
critical to simulate realistic human behaviors. In this work, we address the
challenging problem of generating synchronized object motion and human motion
guided by language descriptions in 3D scenes. We propose Controllable
Human-Object Interaction Synthesis (CHOIS), an approach that generates object
motion and human motion simultaneously using a conditional diffusion model
given a language description, initial object and human states, and sparse
object waypoints. While language descriptions inform style and intent,
waypoints ground the motion in the scene and can be effectively extracted using
high-level planning methods. Naively applying a diffusion model fails to
predict object motion aligned with the input waypoints and cannot ensure the
realism of interactions that require precise hand-object contact and
appropriate contact grounded by the floor. To overcome these problems, we
introduce an object geometry loss as additional supervision to improve the
matching between generated object motion and input object waypoints. In
addition, we design guidance terms to enforce contact constraints during the
sampling process of the trained diffusion model.