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.

本研究提出一种基于几何特征的图卷积网络用于人 - 物交互识别，结合了人体姿态和物体位置等几何要素与视觉特征，在多人和多物体的情况下显著提高了识别准确率，并构建了新的多人人 - 物交互数据集 MPHOI-72。