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.

翻译：人类之间的交互方式，包括人际距离、空间配置和运动模式，在不同情境中存在显著差异。为使机器能够理解此类复杂且依赖上下文的行为，必须对多人及其与周围场景的关系进行建模。本文提出一个新的研究问题：对两个共同参与涉及物体的交互行为的人之间的关联性进行建模，我们将此形式化定义为人-人-物体交互。为克服现有数据集中缺乏专门HHOI数据的问题，我们提出了一个新采集的HHOI数据集，以及利用图像生成模型合成HHOI数据的方法。作为中间步骤，我们从HHOI中提取单个人-物体交互和人-人交互数据，并基于分数扩散模型训练文本到HOI及文本到HHI的生成模型。最终，我们提出一个统一生成框架，整合两个独立模型，能够在单一高级采样过程中合成完整的HHOI。该方法将HHOI生成扩展至多人场景，支持超过两个个体的交互行为。实验结果表明，我们的方法能根据文本描述生成逼真的HHOI，其性能优于仅关注单人HOI的现有方法。此外，我们展示了将框架应用于涉及物体的多人运动生成任务。