TalkVid: A Large-Scale Diversified Dataset for Audio-Driven Talking Head
  Synthesis
Authors: name: Shunian Chen, name: Hejin Huang, name: Yexin Liu, name: Zihan Ye, name: Pengcheng Chen, name: Chenghao Zhu, name: Michael Guan, name: Rongsheng Wang, name: Junying Chen, name: Guanbin Li, name: Ser-Nam Lim, name: Harry Yang, name: Benyou Wang

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