Surgical Video Generation From Diffusion to World Models: A Survey

2026-08-26

AI Summary

本文通过无条件生成、有条件生成和世界模型生成三种方法解决手术视频数据稀缺问题，提高手术模拟与训练的质量。

Abstract

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.

Problem

• Surgical Video Generation
• Data Scarcity
• Clinical Plausibility
• Generalization
• Physical Realism

Innovation

• surgical video generation
• causal dynamics modeling
• data scarcity