# MedGen: Unlocking Medical Video Generation by Scaling Granularly-annotated Medical Videos

arXiv: https://arxiv.org/abs/2507.05675 · HTML: https://mlsanity.com/paper/2507.05675/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 32 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Builds on (most cited first, 27 of 27)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://mlsanity.com/paper/2412.03603/) (2024)
- [ModelScope Text-to-Video Technical Report](https://mlsanity.com/paper/2308.06571/) (2023)
- [LTX-Video: Realtime Video Latent Diffusion](https://mlsanity.com/paper/2501.00103/) (2024)
- [VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models](https://mlsanity.com/paper/2401.09047/) (2024)
- [Latte: Latent Diffusion Transformer for Video Generation](https://mlsanity.com/paper/2401.03048/) (2024)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation](https://mlsanity.com/paper/2407.02371/) (2024)
- [Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives](https://mlsanity.com/paper/2211.04894/) (2022)
- [Open-Sora Plan: Open-Source Large Video Generation Model](https://mlsanity.com/paper/2412.00131/) (2024)
- [VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation](https://mlsanity.com/paper/2406.15252/) (2024)
- [Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos](https://mlsanity.com/paper/2109.03223/) (2021)
- [Allegro: Open the Black Box of Commercial-Level Video Generation Model](https://mlsanity.com/paper/2410.15458/) (2024)
- [Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Models](https://mlsanity.com/paper/2405.05374/) (2024)
- [Vchitect-2.0: Parallel Transformer for Scaling Up Video Diffusion Models](https://mlsanity.com/paper/2501.08453/) (2025)
- [Endora: Video Generation Models as Endoscopy Simulators](https://mlsanity.com/paper/2403.11050/) (2024)
- [PANDA: A Gigapixel-level Human-centric Video Dataset](https://mlsanity.com/paper/2003.04852/) (2020)
- [The SARAS Endoscopic Surgeon Action Detection (ESAD) dataset: Challenges and methods](https://mlsanity.com/paper/2104.03178/) (2021)
- [SurGen: Text-Guided Diffusion Model for Surgical Video Generation](https://mlsanity.com/paper/2408.14028/) (2024)
- [Relevance Detection in Cataract Surgery Videos by Spatio-Temporal Action Localization](https://mlsanity.com/paper/2104.14280/) (2021)
- [Bora: Biomedical Generalist Video Generation Model](https://mlsanity.com/paper/2407.08944/) (2024)
- [PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery](https://mlsanity.com/paper/2409.01184/) (2024)
- [Optical Flow Representation Alignment Mamba Diffusion Model for Medical Video Generation](https://mlsanity.com/paper/2411.01647/) (2024)

## Built on by (most cited first, 9 of 9)

- [A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers](https://mlsanity.com/paper/2508.21148/) (2025)
- [Fleming-VL: Towards Universal Medical Visual Reasoning with Multimodal LLMs](https://mlsanity.com/paper/2511.00916/) (2025)
- [From Articulated Kinematics to Routed Visual Control for Action-Conditioned Surgical Video Generation](https://mlsanity.com/paper/2605.08712/) (2026)
- [VIOLA: Towards Video In-Context Learning with Minimal Annotations](https://mlsanity.com/paper/2601.15549/) (2026)
- [MedScope: Incentivizing "Think with Videos" for Clinical Reasoning via Coarse-to-Fine Tool Calling](https://mlsanity.com/paper/2602.13332/) (2026)
- [MedClaw: Heuristic Agent Harness for Long-Horizon Surgical Video Reasoning](https://mlsanity.com/paper/2608.14015/) (2026)
- [MicroVerse: A Preliminary Exploration Toward a Micro-World Simulation](https://mlsanity.com/paper/2603.00585/) (2026)
- [ReGenHuman: Re-Generating Human Appearances for Realistic Full-Body Video Anonymization](https://mlsanity.com/paper/2606.14972/) (2026)
- [REMEDY: How Far Is Video Generation from Medical Education World Models?](https://mlsanity.com/paper/2609.32460/) (2026)

## Abstract (arXiv)

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