# Surgical Video Generation From Diffusion to World Models: A Survey

arXiv: https://arxiv.org/abs/2608.26214 · HTML: https://mlsanity.com/paper/2608.26214/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical and surgical world models, one of 11 directions in World models.
- Builds on 14 of its 19 references that are in the map.
- First posted on arXiv 26 Aug 2026.

## Placed in

- World models / [Medical and surgical world models](https://mlsanity.com/wm/medical-and-surgical-world-models/)

## Builds on (most cited first, 14 of 14)

- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [Surgical Data Science -- from Concepts toward Clinical Translation](https://mlsanity.com/paper/2011.02284/) (2020)
- [Endora: Video Generation Models as Endoscopy Simulators](https://mlsanity.com/paper/2403.11050/) (2024)
- [SurgSora: Object-Aware Diffusion Model for Controllable Surgical Video Generation](https://mlsanity.com/paper/2412.14018/) (2024)
- [VISAGE: Video Synthesis using Action Graphs for Surgery](https://mlsanity.com/paper/2410.17751/) (2024)
- [How Far Are Surgeons from Surgical World Models? A Pilot Study on Zero-shot Surgical Video Generation with Expert Assessment](https://mlsanity.com/paper/2511.01775/) (2025)
- [Ophora: A Large-Scale Data-Driven Text-Guided Ophthalmic Surgical Video Generation Model](https://mlsanity.com/paper/2505.07449/) (2025)
- [Large-scale Self-supervised Video Foundation Model for Intelligent Surgery](https://mlsanity.com/paper/2506.02692/) (2025)
- [Gradient Harmonization in Unsupervised Domain Adaptation](https://mlsanity.com/paper/2408.00288/) (2024)
- [Interactive Generation of Laparoscopic Videos with Diffusion Models](https://mlsanity.com/paper/2406.06537/) (2024)
- [HieraSurg: Hierarchy-Aware Diffusion Model for Surgical Video Generation](https://mlsanity.com/paper/2506.21287/) (2025)
- [SG2VID: Scene Graphs Enable Fine-Grained Control for Video Synthesis](https://mlsanity.com/paper/2506.03082/) (2025)
- [Unsupervised Robust Domain Adaptation: Paradigm, Theory and Algorithm](https://mlsanity.com/paper/2511.11009/) (2025)
- [SWoMo: Neuro-Symbolic World Model for Cataract Surgery Simulation](https://mlsanity.com/paper/2605.16530/) (2026)

## Abstract (arXiv)

Surgical video data provides the primary training resource for models of intraoperative perception, surgical workflow understanding, and robotic decision-making. However, clinical data acquisition remains constrained by privacy, cost, and class imbalance. Surgical video generation has emerged as a transformative approach to addressing data scarcity and as a foundation for surgical simulation, training, and robotic policy learning. The field has developed rapidly without a clear conceptual framework. This survey organizes the 2024-2026 literature into three categories: unconditional generation, conditional generation, and world modeling generation, revealing a fundamental shift in how the task is defined from synthesizing visually plausible frames to modeling the causal dynamics of surgical scenes. We examine the persistent gap between pixel-level fidelity and clinical plausibility, and identify generalization, physical realism, controllability, and interpretability as bottlenecks. We further summarize experimental results of representative methods on public datasets to provide a quantitative reference for the field. This survey provides a structured overview of the current state and open challenges, offering a reference for researchers working at the intersection of intelligent perception, multi-modal fusion, generative AI, and surgical data science.