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

Research Medical/Healthcare AI

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

Merged summary

TL;DR - This paper introduces a preliminary surgical world-action model that jointly predicts future operative video frames and instrument trajectories. Joint forecasting could improve motion planning by capturing both tool movement and its visual consequences.

  • Historical video frames and tool trajectories are encoded jointly, processed by a temporal-spatial encoder, and decoded through separate visual and trajectory heads.
  • A chunked autoregressive rollout predicts 15 future steps and consistently outperforms direct one-shot prediction across evaluated horizons.
  • For the first segment, chunking raises PSNR from 18.86 to 23.11 dB and reduces average displacement error from 45.77 to 22.22 pixels.
  • Longer forecasts still suffer from progressive visual degradation and accumulated trajectory errors.

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

arXiv cs.CV Weiliang Huang, Huanrong Liu, Bob Zhang, Qi Dou, Zhen Chen, Yun Gu, Guy Rosman, Qingbiao Li 2026-08-20 arXiv:2608.20284
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-10 14:24:23.510521 UTC

TL;DR - This paper introduces a preliminary surgical world-action model that jointly predicts future operative video frames and instrument trajectories. Joint forecasting could improve motion planning by capturing both tool movement and its visual consequences.

  • Historical video frames and tool trajectories are encoded jointly, processed by a temporal-spatial encoder, and decoded through separate visual and trajectory heads.
  • A chunked autoregressive rollout predicts 15 future steps and consistently outperforms direct one-shot prediction across evaluated horizons.
  • For the first segment, chunking raises PSNR from 18.86 to 23.11 dB and reduces average displacement error from 45.77 to 22.22 pixels.
  • Longer forecasts still suffer from progressive visual degradation and accumulated trajectory errors.
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