Towards Surgical World-Action Modeling: A Preliminary Joint Visual-Trajectory Forecasting for Surgical Motion Planning

📄 arXiv: 2608.20284v1 📥 PDF

作者: Weiliang Huang, Huanrong Liu, Bob Zhang, Qi Dou, Zhen Chen, Yun Gu, Guy Rosman, Qingbiao Li

分类: cs.CV, cs.RO

发布日期: 2026-08-20

💡 一句话要点

提出联合视觉-轨迹预测模型以解决外科手术规划问题

🎯 匹配领域: 支柱一：机器人控制 (Robot Control)

关键词: 外科手术规划 视觉-轨迹预测 动态场景建模 时空编码器 自回归预测 机器学习 计算机视觉

📋 核心要点

  1. 现有方法将未来场景生成与工具轨迹预测分开，无法有效评估轨迹与视觉演变之间的一致性。
  2. 本文提出了一种联合视觉-轨迹模型，能够同时预测未来的视觉状态和工具轨迹，提供更全面的手术动态描述。
  3. 实验结果显示，该模型在多个指标上表现优异，PSNR从18.86提升至23.11 dB，ADE从45.77减少至22.22像素。

📝 摘要（中文）

可靠的外科手术规划需要模型不仅预测工具的运动，还需预测手术视觉状态的演变。现有方法通常将未来场景生成与工具轨迹预测视为两个独立任务，导致无法有效评估轨迹与视觉演变之间的一致性。为此，本文提出了一种初步的联合视觉-轨迹世界动作模型，能够从历史手术观察中同时预测未来的视觉状态和工具轨迹。通过对历史视频帧和工具轨迹进行编码，利用时空编码器进行处理，并通过独立的视觉状态和轨迹预测头进行解码。实验结果表明，该模型在多个评估指标上优于传统的一次性预测方法，显示了联合视觉-运动预测的初步可行性。

🔬 方法详解

问题定义：本文旨在解决现有外科手术规划模型在未来场景生成与工具轨迹预测之间缺乏一致性的问题。现有方法无法同时考虑视觉状态与工具运动的动态变化，导致预测效果不佳。

核心思路：提出一种联合视觉-轨迹预测模型，能够从历史手术视频中提取信息，进行未来视觉状态和工具轨迹的同步预测。这种设计旨在提高预测的准确性和一致性。

技术框架：模型首先对历史视频帧和工具轨迹进行编码，生成潜在表示。接着，利用时空编码器处理这些表示，并通过独立的视觉状态和轨迹预测头进行解码，最终实现未来状态的预测。

关键创新：本研究的主要创新在于将视觉状态与轨迹预测联合建模，克服了传统方法的局限性，使得模型能够在轨迹层面进行明确的评估。

关键设计：模型采用了分块自回归的预测策略，能够在多个预测步长上进行有效的预测。实验中，PSNR和ADE等指标的显著提升表明了该设计的有效性。具体参数设置和损失函数的选择也经过精心设计，以优化模型性能。

🖼️ 关键图片

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📊 实验亮点

实验结果显示，采用分块自回归策略的模型在多个预测步长上表现优异，PSNR从18.86提升至23.11 dB，ADE从45.77减少至22.22像素。这些结果表明联合视觉-运动预测的初步可行性，并为未来研究奠定了基础。

🎯 应用场景

该研究的潜在应用领域包括外科手术机器人、手术模拟与培训系统等。通过提高手术规划的准确性和可靠性，能够为外科医生提供更好的决策支持，进而提升手术成功率和患者安全性。未来，该模型可能在其他动态场景预测任务中也具有广泛的应用价值。

📄 摘要（原文）

Reliable surgical planning requires models to anticipate not only how instruments will move, but also how the operative visual state will evolve together with such motion. Existing approaches typically treat future scene generation and instrument trajectory prediction as two separate tasks. Scene-only models cannot directly evaluate the accuracy of future instrument motion at the trajectory level, while trajectory-only models fail to capture the visual consequences of instrument movement, leaving the consistency between predicted trajectories and future scene evolution unaddressed. Jointly forecasting both provides a more complete account of surgical action-scene dynamics by enabling explicit trajectory-level evaluation while simultaneously modeling the corresponding visual evolution. To bridge this gap, we present a preliminary joint visual-trajectory world-action model that simultaneously forecasts future visual states and instrument trajectories from historical surgical observations. Specifically, we encode historical video frames and tool trajectories into latent representations, which are processed by a temporal-spatial encoder and subsequently decoded through separate visual-state and trajectory prediction heads. Based on this preliminary architecture, a chunked autoregressive rollout is repeatedly applied to predict fifteen future steps. The chunked strategy consistently outperforms direct one-shot prediction across all evaluated horizons, improving first-segment PSNR from 18.86 to 23.11 dB and reducing ADE from 45.77 to 22.22 pixels. These results demonstrate the initial feasibility of joint visual-motion forecasting. However, we observe progressive visual degradation and accumulated trajectory errors over longer prediction horizons, which remain important challenges for future surgical world-action modeling.