mlsanityData to 7 Oct 2026

Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos

arXiv 2207.09425 ↗First posted 19 Jul 2022cs.CVcited by 17

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Abstract (arXiv)

Human-Object Interaction (HOI) recognition in videos is important for analyzing human activity. Most existing work focusing on visual features usually suffer from occlusion in the real-world scenarios. Such a problem will be further complicated when multiple people and objects are involved in HOIs. Consider that geometric features such as human pose and object position provide meaningful information to understand HOIs, we argue to combine the benefits of both visual and geometric features in HOI recognition, and propose a novel Two-level Geometric feature-informed Graph Convolutional Network (2G-GCN). The geometric-level graph models the interdependency between geometric features of humans and objects, while the fusion-level graph further fuses them with visual features of humans and objects. To demonstrate the novelty and effectiveness of our method in challenging scenarios, we propose a new multi-person HOI dataset (MPHOI-72). Extensive experiments on MPHOI-72 (multi-person HOI), CAD-120 (single-human HOI) and Bimanual Actions (two-hand HOI) datasets demonstrate our superior performance compared to state-of-the-arts.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 43 of its 61 references that are in the map. First posted on arXiv 19 Jul 2022.

Builds on

43 of 61 references.

References · 43

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31 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 13; 2026: 1.

17 papers.

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  1. 2025HOIGen-1M: A Large-scale Dataset for Human-Object Interaction Video Generationcited by 26
  2. 2025Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Predictioncited by 13
  3. 2025BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Predictioncited by 9
  4. 2025Hierarchical and Multimodal Data for Daily Activity Understandingcited by 6
  5. 2025Learning Human-Object Interaction as Groupscited by 5
  6. 2024From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videoscited by 4
  7. 2025ViTE: Virtual Graph Trajectory Expert Router for Pedestrian Trajectory Predictioncited by 4
  8. 2024Understanding Human Activity with Uncertainty Measure for Novelty in Graph Convolutional Networkscited by 3
  9. 2025ReaMOT: A Benchmark and Framework for Reasoning-based Multi-Object Trackingcited by 3
  10. 2025Geometric Visual Fusion Graph Neural Networks for Multi-Person Human-Object Interaction Recognition in Videoscited by 3
  11. 2025Egocentric Human-Object Interaction Detection: A New Benchmark and Methodcited by 3
  12. 2024Adaptive Graph Learning from Spatial Information for Surgical Workflow Anticipationcited by 2
5 more
  1. 2025Towards Open-World Human Action Segmentation Using Graph Convolutional Networkscited by 1
  2. 2025Real-Time Manipulation Action Recognition with a Factorized Graph Sequence Encodercited by 0
  3. 2025Multi-Modal Graph Convolutional Network with Sinusoidal Encoding for Robust Human Action Segmentationcited by 0
  4. 2025Improvement of Human-Object Interaction Action Recognition Using Scene Information and Multi-Task Learning Approachcited by 0
  5. 2026Cross-view Multimodal Vision-Based Assessment Framework for Traditional Chinese Medicine Rehabilitation Trainingcited by 0