CelebV-HQ

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 represen

大规模数据集在近期人脸生成与编辑的成功中扮演了不可或缺的角色，并极大地推动了新兴研究领域的进展。然而，学术界仍缺乏具有多样化面部属性标注的视频数据集，这对于面部相关视频的研究至关重要。在本研究中，我们提出了一种大规模、高质量且多样化的视频数据集，其中包含丰富的面部属性标注，命名为“高质量名人视频数据集”（CelebV-HQ）。CelebV-HQ包含至少分辨率为512x512的35,666个视频片段，涉及15,653个身份。所有视频片段均经过人工标注了83个面部属性，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、头部姿态多样性和数据质量等方面进行了全面分析，以展示CelebV-HQ的多样性和时间一致性。此外，我们还通过两个代表性任务验证了其通用性和潜力。