Intention recognition has traditionally focused on individual intentions, overlooking the complexities of collective intentions in group settings. To address this limitation, we introduce the concept of group intention, which represents shared goals emerging through the actions of multiple individuals, and Group Intention Forecasting (GIF), a novel task that forecasts when group intentions will occur by analyzing individual actions and interactions before the collective goal becomes apparent. To investigate GIF in a specific scenario, we propose SHOT, the first large-scale dataset for GIF, consisting of 1,979 basketball video clips captured from 5 camera views and annotated with 6 types of individual attributes. SHOT is designed with 3 key characteristics: multi-individual information, multi-view adaptability, and multi-level intention, making it well-suited for studying emerging group intentions. Furthermore, we introduce GIFT (Group Intention ForecasTer), a framework that extracts fine-grained individual features and models evolving group dynamics to forecast intention emergence. Experimental results confirm the effectiveness of SHOT and GIFT, establishing a strong foundation for future research in group intention forecasting. The dataset is available at https://xinyi-hu.github.io/SHOT_DATASET.

翻译：意图识别传统上聚焦于个体意图，忽视了群体环境中集体意图的复杂性。为弥补这一局限，我们引入了群体意图的概念——即通过多个个体行为涌现的共享目标，以及群体意图预测这一新颖任务，该任务通过分析集体目标显现前的个体行为与互动，预测群体意图何时发生。为在特定场景中探究群体意图预测，我们提出了首个大规模群体意图预测数据集SHOT，包含从5个摄像机视角采集的1,979个篮球视频片段，并标注了6类个体属性。SHOT具备三大关键特性：多主体信息、多视角适应性与多层次意图，使其特别适用于研究涌现型群体意图。此外，我们提出了GIFT框架，该框架通过提取细粒度个体特征并建模动态演化的群体交互来预测意图涌现。实验结果验证了SHOT与GIFT的有效性，为群体意图预测的未来研究奠定了坚实基础。数据集可通过https://xinyi-hu.github.io/SHOT_DATASET获取。