Geometric Visual Fusion Graph Neural Networks for Multi-Person Human-Object Interaction Recognition in Videos
🤖 AI Summary
This work addresses the video-based multi-person–object interaction (HOI) recognition task. We propose a Geometry-Visual Graph Neural Network (GV-GNN) that jointly models 3D human pose geometry, visual appearance features, and spatiotemporal dynamics across persons and objects. Methodologically, we explicitly incorporate 3D pose geometric priors into dynamic graph construction, design a cross-subject interaction attention mechanism, and integrate multi-scale spatiotemporal convolutions with differentiable geometric graph pooling for fine-grained joint inference. On CAD-120, V-COCO, and HICO-DET, GV-GNN achieves consistent mAP improvements of 3.2–5.7%, significantly enhancing robustness to occlusion and dense interactions. To our knowledge, this is the first work to systematically embed explicit 3D geometric priors into HOI graph modeling, establishing a novel multimodal spatiotemporal interaction understanding paradigm.
Application Category
Problem
Research questions and friction points this paper is trying to address.
Fusing visual and geometric features for HOI recognition
Handling multi-person concurrent interactions in videos
Addressing occlusion and dynamic human-object relationships
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dual-attention feature fusion for multimodal integration
Interdependent entity graph learning for interaction modeling
Concurrent Partial Interaction Dataset for real-world scenarios
🔎 Similar Papers
No similar papers found.
💼 Related Jobs
