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.

本文提出用于人类与场景中物体进行交互的逼真三维运动生成方法，提出了一种基于神经交互场的技术，该技术可以指导对象条件化的人类运动扩散模型的抽样，以鼓励合理接触和可发掘意义，在支持使用少有数据的情况下，用自动合成数据流水线进行这项研究，最终提出了一种名为 NIFTY（神经交互场用于轨迹综合）的框架。

NIFTY: 导向人体运动综合的神经对象交互场