NIFTY：用于引导人类动作合成的神经对象交互场

摘要

我们解决了在场景中生成人与物体互动的逼真3D动作的问题。我们的关键思想是创建一个附加到特定物体的神经交互场，该场根据输入的人体姿势输出到有效交互流形的距离。这种交互场引导了基于物体条件的人体运动扩散模型的采样，以鼓励合理的接触和功能语义。为了支持与稀缺数据的互动，我们提出了一个自动化合成数据流水线。为此，我们使用从有限动作捕捉数据中提取的与交互特定的锚定姿势来为基础的预训练运动模型进行种子化。利用我们在生成的合成数据上训练的引导扩散模型，我们合成了关于坐姿和举起多个物体的逼真动作，优于其他方法在动作质量和成功动作完成方面。我们将我们的框架称为NIFTY：用于轨迹合成的神经交互场。

English

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.

NIFTY：用于引导人类动作合成的神经对象交互场

NIFTY: Neural Object Interaction Fields for Guided Human Motion Synthesis

摘要

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