  This paper introduces UDIVA, a new non-acted dataset of face-to-face dyadic
interactions, where interlocutors perform competitive and collaborative tasks
with different behavior elicitation and cognitive workload. The dataset
consists of 90.5 hours of dyadic interactions among 147 participants
distributed in 188 sessions, recorded using multiple audiovisual and
physiological sensors. Currently, it includes sociodemographic, self- and
peer-reported personality, internal state, and relationship profiling from
participants. As an initial analysis on UDIVA, we propose a transformer-based
method for self-reported personality inference in dyadic scenarios, which uses
audiovisual data and different sources of context from both interlocutors to
regress a target person's personality traits. Preliminary results from an
incremental study show consistent improvements when using all available context
information.
