论文标题
Celebv-HQ：大型视频面部属性数据集
CelebV-HQ: A Large-Scale Video Facial Attributes Dataset
论文作者
论文摘要
大规模数据集在面部生成/编辑的最新成功中扮演着必不可少的角色，并显着促进了新兴研究领域的进步。但是，学术界仍然缺乏具有不同面部属性注释的视频数据集，这对于与面部相关视频的研究至关重要。在这项工作中，我们提出了一个具有丰富面部属性注释的大规模，高质量和多样化的视频数据集，名为高质量的名人视频数据集（CelebV-HQ）。 Celebv-HQ至少包含35,666个视频剪辑，分辨率为512x512，涉及15,653个身份。所有剪辑都用83个面部属性手动标记，涵盖外观，动作和情感。我们对年龄，种族，亮度稳定性，运动平滑度，头部姿势多样性和数据质量进行了全面分析，以证明CelebV-HQ的多样性和时间连贯性。此外，它的多功能性和潜力在两个代表性任务（即无条件的视频生成和视频面部属性编辑）上得到了验证。此外，我们设想了Celebv-HQ的未来潜力，以及它将带来相关研究方向的新机遇和挑战。数据，代码和模型公开可用。项目页面：https：//celebv-hq.github.io。
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.
