发表机构
University of Macau; National University of Singapore; Xiamen University（澳门大学; 新加坡国立大学; 厦门大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出SurgWMBench，一种面向短程手术运动规划的视觉基准，可评估手术世界模型的器械运动预测与连续回推稳定性，解决现有指标与器械规划需求不匹配的问题。
AI 中文摘要
可靠的手术规划需要模型超越识别当前手术步骤或模仿专家演示，转而预测器械运动如何重塑后续手术状态。多数手术视频理解方法聚焦于识别阶段、动作或工作流状态，对显式建模器械运动的支持有限；现有工具运动预测方法虽能预测器械轨迹，但通常无法捕捉未来手术视频状态的耦合演化。世界模型为联合建模视觉状态转换与器械运动动力学提供了自然框架，不过现有手术世界模型研究大多聚焦于视觉生成质量，依赖FVD、CD-FVD等面向生成的指标，这些指标与器械运动规划的契合度差，无法直接衡量预测轨迹是否几何准确、时间连贯或可用于下游规划。该领域缺乏公共数据集与标准化评估协议，是导致此局限的部分结构性原因，而这些正是评估手术世界模型以运动为中心的能力所需的基准基础设施。本文提出SurgWMBench，这是一个面向短程手术运动规划与动力学预测的视觉基准，给定术中图像序列与历史器械轨迹，SurgWMBench可评估近未来器械运动预测性能，以及连续回推或输入扰动下的稳定性。
英文摘要
Reliable surgical planning requires models that move beyond recognizing the current surgical step or imitating expert demonstrations, and instead anticipate how instrument motion reshapes subsequent operative states. Most surgical video understanding methods focus on recognizing phases, actions, or workflow states, while providing limited support for explicitly modeling instrument motion. Conversely, existing tool motion prediction methods can forecast instrument trajectories, but they generally do not capture the coupled evolution of future surgical video states. World models offer a natural framework for jointly modeling visual state transitions and instrument motion dynamics. However, existing surgical world model studies remain largely centered on visual generation quality, relying on generation-oriented metrics such as FVD and CD-FVD. These metrics are poorly aligned with instrument motion planning, as they do not directly measure whether predicted trajectories are geometrically accurate, temporally coherent, or actionable for downstream planning. This limitation is partly structural, since the field lacks public datasets and standardized evaluation protocols that provide the benchmarking infrastructure needed to assess motion-centric capabilities in surgical world models. In this paper, we introduce SurgWMBench, a vision-based benchmark for short-horizon surgical motion planning and dynamics prediction. Given intraoperative image sequences and historical instrument trajectory, SurgWMBench evaluates both near-future instrument motion prediction and stability under continuous rollout or input perturbations.
