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facial text-video dataset

A facial text-video dataset is a curated collection of video clips focused on human faces paired with corresponding natural language text descriptions. In these datasets, the textual annotations detail both static attributes, such as physical appearance, hairstyle, and accessories, as well as dynamic characteristics, including facial expressions, speech-related motions, and head movements across time. By providing aligned visual and linguistic information, facial text-video datasets serve as foundational resources in computer vision and artificial intelligence for training, evaluating, and benchmarking models designed for text-driven face video generation, facial editing, video animation, and multimodal cross-modal retrieval.

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CelebV-Text: A Large-Scale Facial Text-Video Dataset

CelebV-Text: A Large-Scale Facial Text-Video Dataset

Jianhui Yu, Hao Zhu, Liming Jiang, Chen Change Loy, Weidong Cai, Wayne Wu

Why you should read this

Presents a large-scale dataset of 70,000 in-the-wild facial video clips paired with 1.4 million detailed static and dynamic text descriptions to advance and standardize face-centric text-to-video generation.

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^1.

Added

2026-09-26