CelebV-Text: A Large-Scale Facial Text-Video Dataset
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Cited by 32 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
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.
Key facts. Cited by 104 papers in the map (data through 14 Oct 2026). Builds on 52 of its 81 references that are in the map. First posted on arXiv 26 Mar 2023.
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52 of 81 references.
References · 52
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