Audio-driven talking head synthesis has achieved remarkable photorealism, yet state-of-the-art (SOTA) models exhibit a critical failure: they lack generalization to the full spectrum of human diversity in ethnicity, language, and age groups. We argue that this generalization gap is a direct symptom of limitations in existing training data, which lack the necessary scale, quality, and diversity. To address this challenge, we introduce TalkVid, a new large-scale, high-quality, and diverse dataset containing 1244 hours of video from 7729 unique speakers. TalkVid is curated through a principled, multi-stage automated pipeline that rigorously filters for motion stability, aesthetic quality, and facial detail, and is validated against human judgments to ensure its reliability. Furthermore, we construct and release TalkVid-Bench, a stratified evaluation set of 500 clips meticulously balanced across key demographic and linguistic axes. Our experiments demonstrate that a model trained on TalkVid outperforms counterparts trained on previous datasets, exhibiting superior cross-dataset generalization. Crucially, our analysis on TalkVid-Bench reveals performance disparities across subgroups that are obscured by traditional aggregate metrics, underscoring its necessity for future research. Code and data can be found in https://github.com/FreedomIntelligence/TalkVid
@article{arxiv.2508.13618,
title = {TalkVid: A Large-Scale Diversified Dataset for Audio-Driven Talking Head Synthesis},
author = {Shunian Chen and Hejin Huang and Yexin Liu and Zihan Ye and Pengcheng Chen and Chenghao Zhu and Michael Guan and Rongsheng Wang and Junying Chen and Guanbin Li and Ser-Nam Lim and Harry Yang and Benyou Wang},
journal= {arXiv preprint arXiv:2508.13618},
year = {2025}
}