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NIFTY: Neural Object Interaction Fields for Guided Human Motion Synthesis

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arxiv 2307.07511 v1 pith:NO4ZM345 submitted 2023-07-14 cs.CV

Nilesh Kulkarni , Davis Rempe , Kyle Genova , Abhijit Kundu , Justin Johnson , David Fouhey , Leonidas Guibas This is my paper · ORCID
classification cs.CV
keywords interactionmotiondatahumanmodelneuraldiffusionfield
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Diffgrasp: Whole-Body Grasping Synthesis Guided by Object Motion Using a Diffusion Model

    cs.CV 2024-12 conditional novelty 7.0 of 10

    DiffGrasp synthesizes full-body grasping motion sequences with realistic hand-object contact from object shape and motion via a single conditional diffusion model.

  2. InterAct: Advancing Large-Scale Versatile 3D Human-Object Interaction Generation

    cs.CV 2025-09 conditional novelty 6.0 of 10

    InterAct is a unified 21.81-hour 3D human-object interaction benchmark with text annotations, quality-corrected data, and a multi-task model that achieves state-of-the-art results across six generation tasks.

  3. Mimicking-Bench: A Benchmark for Generalizable Humanoid-Scene Interaction Learning via Human Mimicking

    cs.RO 2024-12 conditional novelty 6.0 of 10

    Mimicking-Bench provides six humanoid-scene interaction tasks with 23K human motion references and a retarget-track-imitate pipeline that beats data-free RL on average success.

  4. TriDi: Trilateral Diffusion of 3D Humans, Objects, and Interactions

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A single diffusion model generates humans, objects, and their interactions in all seven conditioning configurations, outperforming one-way specialized baselines on BEHAVE and GRAB.

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