👓 X-LeBench: A Benchmark for Extremely Long Egocentric Video Understanding

X-LeBench is a novel benchmark and dataset specifically crafted for evaluating tasks on extremely long egocentric video recordings, containing video life logs consist of multiple videos with corresponding timestamps.

🚨 Before You Use the Code

Please refer to our paper and project page for details on task definitions and usage guidelines.

📥 To get started:

• Download the video data from Ego4D
• Download simulation and annotation .json files from our HuggingFace dataset page

🔍 Highlights

• 🎫 Life-logging Simulation Pipeline: a novel and customizable pipeline that simulates realistic, ultra-long egocentric video life logs by integrating synthetic daily plans with real-world footages (from Ego4D).
• 🎞️ Dataset: 432 extremely long video life-log simulations of varying lengths.
• 📖 Benchmark Tasks: 4 daily activity related tasks consisting of 8 subtasks.

📂 Code Structure

X-LeBench/
│── assets/                # Images and shared visual assets
│── generation/            # Life-logging simulation pipeline implementation
│── README.md              # Project entry and setup instructions

📢 Citation

If you use X-LeBench in your research, please cite our work:

@misc{zhou2025xlebenchbenchmarkextremelylong,
      title={X-LeBench: A Benchmark for Extremely Long Egocentric Video Understanding}, 
      author={Wenqi Zhou and Kai Cao and Hao Zheng and Xinyi Zheng and Miao Liu and Per Ola Kristensson and Walterio Mayol-Cuevas and Fan Zhang and Weizhe Lin and Junxiao Shen},
      year={2025},
      eprint={2501.06835},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2501.06835}, 
}

And please also cite Ego4D for their landmark contributions!

@inproceedings{grauman2022ego4d,
  title={Ego4d: Around the world in 3,000 hours of egocentric video},
  author={Grauman, Kristen and Westbury, Andrew and Byrne, Eugene and Chavis, Zachary and Furnari, Antonino and Girdhar, Rohit and Hamburger, Jackson and Jiang, Hao and Liu, Miao and Liu, Xingyu and others},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={18995--19012},
  year={2022}
}

📝 License

This project is licensed under the MIT License. See the LICENSE file for details.