From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos

• URL: http://arxiv.org/abs/2407.00917v2
• Date: Tue, 23 Jul 2024 13:10:28 GMT
• Title: From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos
• Authors: Tanqiu Qiao, Ruochen Li, Frederick W. B. Li, Hubert P. H. Shum,
• Abstract summary: Video-based Human-Object Interaction (HOI) recognition explores the intricate dynamics between humans and objects. In this work, we propose a novel end-to-end category to scenery framework, CATS. We construct a scenery interactive graph with these enhanced geometric-visual features as nodes to learn the relationships among human and object categories.
• Score: 9.159660801125812
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video-based Human-Object Interaction (HOI) recognition explores the intricate dynamics between humans and objects, which are essential for a comprehensive understanding of human behavior and intentions. While previous work has made significant strides, effectively integrating geometric and visual features to model dynamic relationships between humans and objects in a graph framework remains a challenge. In this work, we propose a novel end-to-end category to scenery framework, CATS, starting by generating geometric features for various categories through graphs respectively, then fusing them with corresponding visual features. Subsequently, we construct a scenery interactive graph with these enhanced geometric-visual features as nodes to learn the relationships among human and object categories. This methodological advance facilitates a deeper, more structured comprehension of interactions, bridging category-specific insights with broad scenery dynamics. Our method demonstrates state-of-the-art performance on two pivotal HOI benchmarks, including the MPHOI-72 dataset for multi-person HOIs and the single-person HOI CAD-120 dataset.

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