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

arXiv: https://arxiv.org/abs/2303.14717 · HTML: https://mlsanity.com/paper/2303.14717/

## 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.

## Builds on (most cited first, 30 of 52)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs](https://mlsanity.com/paper/2111.02114/) (2021)
- [CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](https://mlsanity.com/paper/1612.06890/) (2016)
- [Towards Accurate Generative Models of Video: A New Metric & Challenges](https://mlsanity.com/paper/1812.01717/) (2018)
- [Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval](https://mlsanity.com/paper/2104.00650/) (2021)
- [CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers](https://mlsanity.com/paper/2205.15868/) (2022)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Dense-Captioning Events in Videos](https://mlsanity.com/paper/1705.00754/) (2017)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [Localizing Moments in Video with Natural Language](https://mlsanity.com/paper/1708.01641/) (2017)
- [Towards Automatic Learning of Procedures from Web Instructional Videos](https://mlsanity.com/paper/1703.09788/) (2017)
- [Rescaling Egocentric Vision](https://mlsanity.com/paper/2006.13256/) (2020)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Phenaki: Variable Length Video Generation From Open Domain Textual Description](https://mlsanity.com/paper/2210.02399/) (2022)
- [VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research](https://mlsanity.com/paper/1904.03493/) (2019)
- [Quantifying Generalization in Reinforcement Learning](https://mlsanity.com/paper/1812.02341/) (2018)
- [StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2](https://mlsanity.com/paper/2112.14683/) (2021)
- [Flexible Diffusion Modeling of Long Videos](https://mlsanity.com/paper/2205.11495/) (2022)
- [GODIVA: Generating Open-DomaIn Videos from nAtural Descriptions](https://mlsanity.com/paper/2104.14806/) (2021)
- [N"UWA: Visual Synthesis Pre-training for Neural visUal World creAtion](https://mlsanity.com/paper/2111.12417/) (2021)
- [CelebV-HQ: A Large-Scale Video Facial Attributes Dataset](https://mlsanity.com/paper/2207.12393/) (2022)

## Built on by (most cited first, 30 of 104)

- [OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation](https://mlsanity.com/paper/2407.02371/) (2024)
- [A Survey on Video Diffusion Models](https://mlsanity.com/paper/2310.10647/) (2023)
- [Identity-Preserving Text-to-Video Generation by Frequency Decomposition](https://mlsanity.com/paper/2411.17440/) (2024)
- [VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models](https://mlsanity.com/paper/2403.06098/) (2024)
- [ID-Animator: Zero-Shot Identity-Preserving Human Video Generation](https://mlsanity.com/paper/2404.15275/) (2024)
- [PortraitBooth: A Versatile Portrait Model for Fast Identity-preserved Personalization](https://mlsanity.com/paper/2312.06354/) (2023)
- [Detecting Multimedia Generated by Large AI Models: A Survey](https://mlsanity.com/paper/2402.00045/) (2024)
- [Sonic: Shifting Focus to Global Audio Perception in Portrait Animation](https://mlsanity.com/paper/2411.16331/) (2024)
- [Portrait4D-v2: Pseudo Multi-View Data Creates Better 4D Head Synthesizer](https://mlsanity.com/paper/2403.13570/) (2024)
- [Sora as a World Model? A Complete Survey on Text-to-Video Generation](https://mlsanity.com/paper/2403.05131/) (2024)
- [AutoDecoding Latent 3D Diffusion Models](https://mlsanity.com/paper/2307.05445/) (2023)
- [FLOAT: Generative Motion Latent Flow Matching for Audio-driven Talking Portrait](https://mlsanity.com/paper/2412.01064/) (2024)
- [InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity](https://mlsanity.com/paper/2503.16418/) (2025)
- [MagicMirror: ID-Preserved Video Generation in Video Diffusion Transformers](https://mlsanity.com/paper/2501.03931/) (2025)
- [TalkCLIP: Talking Head Generation with Text-Guided Expressive Speaking Styles](https://mlsanity.com/paper/2304.00334/) (2023)
- [MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision](https://mlsanity.com/paper/2308.16139/) (2023)
- [Mavors: Multi-granularity Video Representation for Multimodal Large Language Model](https://mlsanity.com/paper/2504.10068/) (2025)
- [InstantDrag: Improving Interactivity in Drag-based Image Editing](https://mlsanity.com/paper/2409.08857/) (2024)
- [SpeakerVid-5M: A Large-Scale High-Quality Dataset for Audio-Visual Dyadic Interactive Human Generation](https://mlsanity.com/paper/2507.09862/) (2025)
- [EchoShot: Multi-Shot Portrait Video Generation](https://mlsanity.com/paper/2506.15838/) (2025)
- [MultiTalk: Enhancing 3D Talking Head Generation Across Languages with Multilingual Video Dataset](https://mlsanity.com/paper/2406.14272/) (2024)
- [LaughTalk: Expressive 3D Talking Head Generation with Laughter](https://mlsanity.com/paper/2311.00994/) (2023)
- [From Sora What We Can See: A Survey of Text-to-Video Generation](https://mlsanity.com/paper/2405.10674/) (2024)
- [FT2TF: First-Person Statement Text-To-Talking Face Generation](https://mlsanity.com/paper/2312.05430/) (2023)
- [HOIGen-1M: A Large-scale Dataset for Human-Object Interaction Video Generation](https://mlsanity.com/paper/2503.23715/) (2025)
- [Human Motion Video Generation: A Survey](https://mlsanity.com/paper/2509.03883/) (2025)
- [3D Face Tracking from 2D Video through Iterative Dense UV to Image Flow](https://mlsanity.com/paper/2404.09819/) (2024)
- [Joker: Conditional 3D Head Synthesis with Extreme Facial Expressions](https://mlsanity.com/paper/2410.16395/) (2024)
- [Omni-ID: Holistic Identity Representation Designed for Generative Tasks](https://mlsanity.com/paper/2412.09694/) (2024)
- [Audio-visual Controlled Video Diffusion with Masked Selective State Spaces Modeling for Natural Talking Head Generation](https://mlsanity.com/paper/2504.02542/) (2025)

## 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.