Face-to-Face: A Video Dataset for Multi-Person Interaction Modeling

• URL: http://arxiv.org/abs/2603.14794v1
• Date: Mon, 16 Mar 2026 03:50:02 GMT
• Title: Face-to-Face: A Video Dataset for Multi-Person Interaction Modeling
• Abstract summary: We introduce textbfFace-to-Face with Jimmy Fallon (F2F-JF), a 70-hour, 14k-clip dataset of two-person talk-show exchanges.<n>A semi-automatic pipeline combines multi-person tracking, speech diarization, and lightweight human verification to extract temporally aligned host/guest tracks.
• Score: 53.176242285107485
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modeling the reactive tempo of human conversation remains difficult because most audio-visual datasets portray isolated speakers delivering short monologues. We introduce \textbf{Face-to-Face with Jimmy Fallon (F2F-JF)}, a 70-hour, 14k-clip dataset of two-person talk-show exchanges that preserves the sequential dependency between a guest turn and the host's response. A semi-automatic pipeline combines multi-person tracking, speech diarization, and lightweight human verification to extract temporally aligned host/guest tracks with tight crops and metadata that are ready for downstream modeling. We showcase the dataset with a reactive, speech-driven digital avatar task in which the host video during $[t_1,t_2]$ is generated from their audio plus the guest's preceding video during $[t_0,t_1]$. Conditioning a MultiTalk-style diffusion model on this cross-person visual context yields small but consistent Emotion-FID and FVD gains while preserving lip-sync quality relative to an audio-only baseline. The dataset, preprocessing recipe, and baseline together provide an end-to-end blueprint for studying dyadic, sequential behavior, which we expand upon throughout the paper. Dataset and code will be made publicly available.

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