An Image-like Diffusion Method for Human-Object Interaction Detection

• URL: http://arxiv.org/abs/2503.18134v1
• Date: Sun, 23 Mar 2025 16:30:16 GMT
• Title: An Image-like Diffusion Method for Human-Object Interaction Detection
• Authors: Xiaofei Hui, Haoxuan Qu, Hossein Rahmani, Jun Liu,
• Abstract summary: The output of HOI detection for each human-object pair can be recast as an image.<n>In HOI-IDiff, we tackle HOI detection from a novel perspective, using an Image-like Diffusion process to generate HOI detection outputs as images.
• Score: 13.951650101149188
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human-object interaction (HOI) detection often faces high levels of ambiguity and indeterminacy, as the same interaction can appear vastly different across different human-object pairs. Additionally, the indeterminacy can be further exacerbated by issues such as occlusions and cluttered backgrounds. To handle such a challenging task, in this work, we begin with a key observation: the output of HOI detection for each human-object pair can be recast as an image. Thus, inspired by the strong image generation capabilities of image diffusion models, we propose a new framework, HOI-IDiff. In HOI-IDiff, we tackle HOI detection from a novel perspective, using an Image-like Diffusion process to generate HOI detection outputs as images. Furthermore, recognizing that our recast images differ in certain properties from natural images, we enhance our framework with a customized HOI diffusion process and a slice patchification model architecture, which are specifically tailored to generate our recast ``HOI images''. Extensive experiments demonstrate the efficacy of our framework.

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