Latent Interactiveness Field for Non-Contact Human Object Interaction Detection

摘要

Human-Object Interaction (HOI) detection serves a broad spectrum of applications. Despite significant progress, current approaches encounter difficulties in effectively handling Non-Contact Human-Object Interaction (NCHOI) scenarios, where humans and objects remain physically apart. To address these challenges, this paper proposes a novel approach, named Latent Interactiveness Field Modeling (LIFM), which enhances HOI detection by capturing long-range contextual dependencies. Specifically, the Latent Interactiveness Field (LIF) is introduced to define potential interactive relationships between humans and objects. To complement this, the LIF Fusion Encoder is designed to adaptively fuse visual features with LIF, resulting in more informative and discriminative feature representations. The Mobile Scanning HOI Dataset (MSHD) is introduced as a comprehensive benchmark to systematically assess the robustness of existing methods on both common HOI and NCHOI in real-world applications. Extensive experimentation indicates that the proposed approach outperforms existing state-of-the-art techniques. It offers substantial improvements, particularly in NCHOI scenarios, which highlight its effectiveness in resolving issues related to long-range interactions.