NIFTY: Neural Object Interaction Fields for Guided Human Motion Synthesis

Nilesh Kulkarni, Davis Rempe, Kyle Genova, Abhijit Kundu, Justin Johnson, David Fouhey, Leonidas Guibas; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 947-957

We address the problem of generating realistic 3D motions of humans interacting with objects in a scene. Our key idea is to create a neural interaction field attached to a specific object which outputs the distance to the valid interaction manifold given a human pose as input. This interaction field guides the sampling of an object-conditioned human motion diffusion model so as to encourage plausible contacts and affordance semantics. To support interactions with scarcely available data we propose an automated synthetic data pipeline. For this we seed a pre-trained motion model which has priors for the basics of human movement with interaction-specific anchor poses extracted from limited motion capture data. Using our guided diffusion model trained on generated synthetic data we synthesize realistic motions for sitting and lifting with several objects outperforming alternative approaches in terms of motion quality and successful action completion. We call our framework NIFTY: Neural Interaction Fields for Trajectory sYnthesis.