  Text-driven generation models are flourishing in video generation and
editing. However, face-centric text-to-video generation remains a challenge due
to the lack of a suitable dataset containing high-quality videos and highly
relevant texts. This paper presents CelebV-Text, a large-scale, diverse, and
high-quality dataset of facial text-video pairs, to facilitate research on
facial text-to-video generation tasks. CelebV-Text comprises 70,000 in-the-wild
face video clips with diverse visual content, each paired with 20 texts
generated using the proposed semi-automatic text generation strategy. The
provided texts are of high quality, describing both static and dynamic
attributes precisely. The superiority of CelebV-Text over other datasets is
demonstrated via comprehensive statistical analysis of the videos, texts, and
text-video relevance. The effectiveness and potential of CelebV-Text are
further shown through extensive self-evaluation. A benchmark is constructed
with representative methods to standardize the evaluation of the facial
text-to-video generation task. All data and models are publicly available.
