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
Abstract
"Large-scale datasets played an indispensable role in the recent success of face generation/editing and significantly facilitate the advances of emerging research fields. However, the academic community still lacks a video dataset with diverse facial attribute annotations, which is crucial for face-related video research. In this paper, we propose a large-scale, high-quality, and diverse video dataset, named the High-Quality Celebrity Video Dataset (CelebV-HQ), with rich facial attribute annotations. CelebV-HQ contains 35,666 video clips involving 15,653 identities and 83 manually labeled facial attributes covering appearance, action, and emotion. We conduct a comprehensive analysis in terms of ethnicity, age, brightness, 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 unconditional video generation and video facial attribute editing tasks. Furthermore, we envision the future potential of CelebV-HQ, as well as the new opportunities and challenges it would bring to related research directions."
BibTeX
@inproceedings{eccv2022_celebvhqalargesc,
title = {CelebV-HQ: A Large-Scale Video Facial Attributes Dataset},
author = {Hao Zhu and Wayne Wu and Wentao Zhu and Liming Jiang and Siwei Tang and Li Zhang and Ziwei Liu and Chen Change Loy},
booktitle = {ECCV 2022},
year = {2022}
}