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HOI-M3:Capture Multiple Humans and Objects Interaction within Contextual Environment

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arxiv 2404.00299 v2 pith:IRSIBQWZ submitted 2024-03-30 cs.CV

HOI-M3:Capture Multiple Humans and Objects Interaction within Contextual Environment

classification cs.CV
keywords multipleobjectshumansdatasethoi-m3interactionshuman-objectactivities
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Humans naturally interact with both others and the surrounding multiple objects, engaging in various social activities. However, recent advances in modeling human-object interactions mostly focus on perceiving isolated individuals and objects, due to fundamental data scarcity. In this paper, we introduce HOI-M3, a novel large-scale dataset for modeling the interactions of Multiple huMans and Multiple objects. Notably, it provides accurate 3D tracking for both humans and objects from dense RGB and object-mounted IMU inputs, covering 199 sequences and 181M frames of diverse humans and objects under rich activities. With the unique HOI-M3 dataset, we introduce two novel data-driven tasks with companion strong baselines: monocular capture and unstructured generation of multiple human-object interactions. Extensive experiments demonstrate that our dataset is challenging and worthy of further research about multiple human-object interactions and behavior analysis. Our HOI-M3 dataset, corresponding codes, and pre-trained models will be disseminated to the community for future research.

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Cited by 2 Pith papers

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

  1. GIRAF: Towards Generalizable Human Interactions with Articulated Objects

    cs.CV 2026-07 conditional novelty 6.0

    A text-conditioned diffusion model using dynamic object-centric BPS, mixed-domain training, and contact augmentation produces generalizable full-body locomotion-to-articulated-object interaction sequences that beat ad...

  2. MOCHI: Motion Enhancement of Collaborative Human-object Interactions

    cs.CV 2026-06 unverdicted novelty 6.0

    MOCHI enhances noisy collaborative human-object interaction captures via grasp optimization followed by diffusion-based full-body refinement that incorporates interaction information into single-person motion priors.