SurgVista：具有合理器械-组织动力学的长程手术世界建模
SurgVista: Long-Horizon Surgical World Modeling with Plausible Instrument-Tissue Dynamics
AI总结：
提出SurgVista手术世界模型，通过变形一致性正则化和漂移适应训练，解决空间交互不连贯和时间保真度崩溃问题，在长程预测中显著优于现有方法。
摘要：
将机器人策略学习扩展到自主手术面临挑战，因为专家演示成本高昂且体内探索存在重大安全风险。手术世界模型通过从初始观测生成逼真的、动作条件下的未来帧来解决这一问题，但现有方法存在两种持续失效模式：空间交互不连贯，即可见器械接触未能引起空间一致的组织变形；以及时间保真度崩溃，即预测误差在自回归展开中累积并逐渐破坏视觉质量。我们提出SurgVista，一种通过两种训练策略缓解这两种失效的手术世界模型。变形一致性正则化从训练视频中提取场景点轨迹，并通过潜在对比学习强制跨帧一致性，增强物理一致的器械-组织动力学。漂移适应训练通过用在线预测残差和根据长程漂移统计校准的光度增强扰动条件帧，减轻长程漂移，在扩展展开中维持视觉保真度。为了进行严格评估，我们进一步引入SurgWorld-Bench，包含多样化的手术类型、长程展开以及用于器械运动精度和组织响应保真度的解耦指标。大量实验表明，SurgVista在视觉质量、时间一致性和交互保真度方面持续优于最先进方法，且随着预测视界增长优势扩大。
英文摘要：
Scaling robot policy learning for autonomous surgery is challenging, as expert demonstrations are expensive and in vivo exploration poses substantial safety risks. Surgical world models address this by generating realistic, action-conditioned future frames from an initial observation, but existing methods exhibit two persistent failure modes: spatial interaction incoherence, where visible instrument contact fails to induce spatially consistent tissue deformation, and temporal fidelity collapse, where prediction errors compound across autoregressive rollouts and progressively corrupt visual quality. We present SurgVista, a surgical world model that mitigates both failures through two training recipes. Deformation Consistency Regularization extracts scene-point trajectories from training videos and enforces cross-frame coherence through latent contrastive learning, strengthening physically consistent instrument-tissue dynamics. Drift Adaptation Training mitigates long-horizon drift by perturbing conditioning frames with online prediction residuals and photometric augmentations calibrated to long-horizon drift statistics, sustaining visual fidelity over extended rollouts. To enable rigorous evaluation, we further introduce SurgWorld-Bench, featuring diverse procedure types, long-range rollouts, and decoupled metrics for instrument-motion accuracy and tissue-response fidelity. Extensive experiments show that SurgVista consistently outperforms state-of-the-art methods across visual quality, temporal consistency, and interaction fidelity, with gains widening as the prediction horizon grows.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
