The degree of concentration, enthusiasm, optimism, and passion displayed by individual(s) while interacting with a machine is referred to as `user engagement'. Engagement comprises of behavioural, cognitive, and affect related cues. To create engagement predictions systems, which can work in real-world conditions it is quintessential to learn from rich diverse datasets. To this end, a large scale multi-faceted engagement in the wild dataset is proposed. 31 hours duration data of 127 participants representing different illumination conditions is recorded. Thorough experiments are performed exploring applicability of different features action units, eye gaze and head pose and transformers. To further validate the rich nature of the dataset, evaluation is also performed on the EngageWild dataset. The experiments show the usefulness of the proposed dataset. The code, models and dataset will be made publicly available.

翻译：用户在与机器交互过程中表现出的专注度、热情度、乐观度和投入程度的综合表现被称为“用户参与度”。参与度包含行为、认知和情感相关线索。要构建能在真实世界条件下运行的参与度预测系统，从丰富多样的数据集中学习至关重要。为此，本文提出一个大规模、多方面的野外参与度数据集。该数据集记录了127名参与者在不同光照条件下的31小时时长数据。我们通过系统性实验探索了动作单元、视线注视、头部姿态及Transformer等不同特征的可适用性。为进一步验证该数据集的丰富性，我们还在EngageWild数据集上进行了评估。实验结果表明了所提数据集的有效性。相关代码、模型和数据集将公开发布。