FreedomIntelligence/MedGen-1.3B

Unlocking Medical Video Generation by Scaling Granularly-annotated Medical Videos

[📃 Paper] ｜ [🤗 Dataset] ｜ [🤗 MedGen-1.3B] ｜ [🤗 MedGen-14B] ｜ [🚀 Blog]

⚡ Introduction

Recent advances in video generation have shown remarkable progress in open-domain settings, yet medical video generation remains largely underexplored. Medical videos are critical for applications such as clinical training, education, and simulation, requiring not only high visual fidelity but also strict medical accuracy. However, current models often produce unrealistic or erroneous content when applied to medical prompts, largely due to the lack of large-scale, high-quality datasets tailored to the medical domain. To address this gap, we introduce MedVideoCap-55K, the first large-scale, diverse, and caption-rich dataset for medical video generation. It comprises over 55,000 curated clips spanning real-world medical scenarios, providing a strong foundation for training generalist medical video generation models. Built upon this dataset, we develop MedGen, which achieves leading performance among open-source models and rivals commercial systems across multiple benchmarks in both visual quality and medical accuracy. We hope our dataset and model can serve as a valuable resource and help catalyze further research in medical video generation.

🚀Quick Start

Install

pip install -r requirements.txt

Inference

python inference.py --prompt "A doctor examining a patient" --output exam.mp4
python inference.py --batch prompts.json --output_dir results/
python inference.py --prompt "..." --gpu 1 --output result.mp4

🤩 Acknowledgement

Our works are inspired by the following works.

• FastVideo: a lightweight framework for accelerating large video diffusion models.
• DiffSynth-Studio: an open-source Diffusion model engine developed.
• VBench: a comprehensive benchmark suite for video generative models.
• VideoScore: a automatic metrics to simulate fine-grained human feedback for video generation.

📖 Citation

@misc{wang2025medgenunlockingmedicalvideo,
      title={MedGen: Unlocking Medical Video Generation by Scaling Granularly-annotated Medical Videos}, 
      author={Rongsheng Wang and Junying Chen and Ke Ji and Zhenyang Cai and Shunian Chen and Yunjin Yang and Benyou Wang},
      year={2025},
      eprint={2507.05675},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2507.05675}, 
}