Papers › CelebV-HQ: A Large-Scale Video Facial Attributes Dataset
CelebV-HQ: A Large-Scale Video Facial Attributes Dataset
Hao Zhu, Wayne Wu, Wentao Zhu, Liming Jiang, Siwei Tang, Li Zhang, Ziwei Liu, Chen Change Loy
Large-scale datasets have played indispensable roles in the recent success of face generation/editing and significantly facilitated the advances of emerging research fields. However, the academic community still lacks a video dataset with diverse facial attribute annotations, which is crucial for the research on face-related videos. In this work, we propose a large-scale, high-quality, and diverse video dataset with rich facial attribute annotations, named the High-Quality Celebrity Video Dataset (CelebV-HQ). CelebV-HQ contains 35,666 video clips with the resolution of 512x512 at least, involving 15,653 identities. All clips are labeled manually with 83 facial attributes, covering appearance, action, and emotion. We conduct a comprehensive analysis in terms of age, ethnicity, brightness stability, motion smoothness, head pose diversity, and data quality to demonstrate the diversity and temporal coherence of CelebV-HQ. Besides, its versatility and potential are validated on two representative tasks, i.e., unconditional video generation and video facial attribute editing. Furthermore, we envision the future potential of CelebV-HQ, as well as the new opportunities and challenges it would bring to related research directions. Data, code, and models are publicly available. Project page: https://celebv-hq.github.io.
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Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Unconditional Video Generation | CelebV-HQ | StyleGAN-V | FID | 17.95 | #1 of 4 | Archive leaderboard | report |
| Unconditional Video Generation | CelebV-HQ | StyleGAN-V | FVD | 69.17 | #1 of 4 | Archive leaderboard | report |
| Unconditional Video Generation | CelebV-HQ | DIGAN | FID | 19.39 | #2 of 4 | Archive leaderboard | report |
| Unconditional Video Generation | CelebV-HQ | DIGAN | FVD | 72.98 | #2 of 4 | Archive leaderboard | report |
| Unconditional Video Generation | CelebV-HQ | VideoGPT | FID | 52.95 | #3 of 4 | Archive leaderboard | report |
| Unconditional Video Generation | CelebV-HQ | VideoGPT | FVD | 177.89 | #3 of 4 | Archive leaderboard | report |
| Unconditional Video Generation | CelebV-HQ | MoCoGAN-HD | FID | 21.55 | #4 of 4 | Archive leaderboard | report |
| Unconditional Video Generation | CelebV-HQ | MoCoGAN-HD | FVD | 212.41 | #4 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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