A Camera-Native Talking-Head Video Dataset for Various Computer Vision Tasks

Abstract

Talking-head videos constitute a predominant content type in real-time communication, yet publicly available datasets for video processing research in this domain remain scarce and limited in signal fidelity. In this paper, we open-source a camera-native dataset of 838 talking-head recordings (approximately 210 minutes), each 15s in duration, captured from 799 participants across 443 camera-code categories in their natural environments. All recordings are stored using the FFV1 lossless codec, preserving the camera-native signal---uncompressed (24.7%) or MJPEG-encoded (75.3%)---without additional lossy processing. Each recording is annotated with a Mean Opinion Score (MOS) and ten perceptual quality tokens that jointly explain 64.4% of the MOS variance. From this corpus, we curate a stratified benchmarking subset of 120 clips in three content conditions: original, background blur, and background replacement. Codec efficiency evaluation across four datasets and four codecs, namely H.264, H.265, H.266, and AV1, yields VMAF BD-rate savings up to $-71.3$ (H.266) relative to H.264, with significant encoder$\times$dataset (${\eta }_p^2=.112$) and encoder$\times$content condition (${\eta }_p^2=.149$) interactions, demonstrating that both content type and background processing affect compression efficiency. A preliminary super-resolution evaluation with four SR models confirms that the dataset significantly affects absolute performance while preserving model rankings, demonstrating applicability beyond codec benchmarking. The dataset offers 5$\times$ the scale of the largest prior talking-head webcam dataset (838 vs. 160 clips) and preserves the camera-native signal without additional lossy compression, establishing a resource for benchmarking video compression, super-resolution, quality assessment, and enhancement models in real-time communication.