Q-Eval-100K Dataset （CVPR 2025 Oral）

📝 Introduction

The Q-Eval-100K dataset encompasses both text-to-image and text-to-video models, with 960K human annotations specifically focused on visual quality and alignment for 100K instances (60K images and 40K videos).

We utilize multiple popular text-to- image and text-to-video models to ensure diversity, which include FLUX, Lumina-T2X, PixArt, Stable Diffusion 3, Stable Diffusion XL, DALL·E 3, Wanx, Midjourney, Hunyuan-DiT, Kolors, ERNIE-ViLG, CogVideoX, Runway GEN-2, Runway GEN-3, Latte, Kling, Dreamina, Luma, PixVerse, Pika, Stable Video Diffusion, Vidu.

💡 The project has currently released all image and video files, as well as the training set annotations.

🔗 The paper is available on arXiv. 🔥🔥🔥

🌟 Citation

If you find our work useful, please cite our paper as:

@misc{zhang2025qeval100kevaluatingvisualquality,
      title={Q-Eval-100K: Evaluating Visual Quality and Alignment Level for Text-to-Vision Content}, 
      author={Zicheng Zhang and Tengchuan Kou and Shushi Wang and Chunyi Li and Wei Sun and Wei Wang and Xiaoyu Li and Zongyu Wang and Xuezhi Cao and Xiongkuo Min and Xiaohong Liu and Guangtao Zhai},
      year={2025},
      eprint={2503.02357},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.02357}, 
}

💳 License

This project is released under the cc by-nc 4.0. Users should check the LICENSE of each dataset individually to ensure proper usage and compliance.