Learning to Generate Human-Human-Object Interactions from Textual Descriptions

• URL: http://arxiv.org/abs/2511.20446v1
• Date: Tue, 25 Nov 2025 16:17:23 GMT
• Title: Learning to Generate Human-Human-Object Interactions from Textual Descriptions
• Authors: Jeonghyeon Na, Sangwon Baik, Inhee Lee, Junyoung Lee, Hanbyul Joo,
• Abstract summary: We present a novel research problem to model the correlations between two people engaged in a shared interaction involving an object.<n>We refer to this formulation as Human-Human-Object Interactions (HHOIs)<n>We present a newly captured HHOIs dataset and a method to synthesize HHOI data by leveraging image generative models.
• Score: 15.38195247862565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The way humans interact with each other, including interpersonal distances, spatial configuration, and motion, varies significantly across different situations. To enable machines to understand such complex, context-dependent behaviors, it is essential to model multiple people in relation to the surrounding scene context. In this paper, we present a novel research problem to model the correlations between two people engaged in a shared interaction involving an object. We refer to this formulation as Human-Human-Object Interactions (HHOIs). To overcome the lack of dedicated datasets for HHOIs, we present a newly captured HHOIs dataset and a method to synthesize HHOI data by leveraging image generative models. As an intermediary, we obtain individual human-object interaction (HOIs) and human-human interaction (HHIs) from the HHOIs, and with these data, we train an text-to-HOI and text-to-HHI model using score-based diffusion model. Finally, we present a unified generative framework that integrates the two individual model, capable of synthesizing complete HHOIs in a single advanced sampling process. Our method extends HHOI generation to multi-human settings, enabling interactions involving more than two individuals. Experimental results show that our method generates realistic HHOIs conditioned on textual descriptions, outperforming previous approaches that focus only on single-human HOIs. Furthermore, we introduce multi-human motion generation involving objects as an application of our framework.

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