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Recording the dynamics of unscripted human interactions in the wild ischallenging due to the delicate trade-offs between several factors: participantprivacy, ecological validity, data fidelity, and logistical overheads. Toaddress these, following a 'datasets for the community by the community' ethos,we propose the Conference Living Lab (ConfLab): a new concept for multimodalmultisensor data collection of in-the-wild free-standing social conversations.For the first instantiation of ConfLab described here, we organized a real-lifeprofessional networking event at a major international conference. Involving 48conference attendees, the dataset captures a diverse mix of status,acquaintance, and networking motivations. Our capture setup improves upon thedata fidelity of prior in-the-wild datasets while retaining privacysensitivity: 8 videos (1920x1080, 60 fps) from a non-invasive overhead view,and custom wearable sensors with onboard recording of body motion (full 9-axisIMU), privacy-preserving low-frequency audio (1250 Hz), and Bluetooth-basedproximity. Additionally, we developed custom solutions for distributed hardwaresynchronization at acquisition and time-efficient continuous annotation of bodykeypoints and actions at high sampling rates. Our benchmarks showcase some ofthe open research tasks related to in-the-wild privacy-preserving social dataanalysis: keypoints detection from overhead camera views, skeleton-basedno-audio speaker detection, and F-formation detection.

ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild