MedGen: Unlocking Medical Video Generation by Scaling
  Granularly-annotated Medical Videos
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. Our code and data is available at
https://github.com/FreedomIntelligence/MedGen
686ddcfacb5725779c60b427
Rongsheng Wang
claimed_verified
2025-07-09T08:50:04.039Z
63ca949b04c979828315389d
https://cdn-avatars.huggingface.co/v1/production/uploads/63ca949b04c979828315389d/HS5xWNAYjjHeyAAwWJ11l.jpeg
wangrongsheng
user
wangrongsheng
686ddcfacb5725779c60b428
Junying Chen
686ddcfacb5725779c60b429
Ke Ji
claimed_verified
2025-09-11T16:12:57.434Z
64c24a1b3499e4f3a2fb680c
/avatars/7859c3164071a1797c4d9271c8c6ac2e.svg
Ke Ji
user
kehorizon
686ddcfacb5725779c60b42a
Zhenyang Cai
686ddcfacb5725779c60b42b
Shunian Chen
686ddcfacb5725779c60b42c
Yunjin Yang
686ddcfacb5725779c60b42d
Benyou Wang
MedGen: Unlocking Medical Video Generation by Scaling
  Granularly-annotated Medical Videos
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. Our code and data is available at
https://github.com/FreedomIntelligence/MedGen
MedGen, a model trained on MedVideoCap-55K, achieves top performance in medical video generation, balancing visual quality and medical accuracy.
MedVideoCap-55K MedGen medical video generation visual quality medical accuracy benchmarks