[Submitted on 3 Nov 2026]
Title:SurgTransfer: Multi-Step Latent Action Modeling for Action Transfer in Surgical World Model
View a PDF of the paper titled SurgTransfer: Multi-Step Latent Action Modeling for Action Transfer in Surgical World Model, by Saurabh Koju and 8 other authors
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Abstract:Surgical world models have the potential to provide intra-operative guidance, accelerate risk-free training and facilitate transfer of expert surgical maneuvers. However, the use of world models for transferring surgical expertise has been largely unexplored. Moreover, recent approaches in this domain have mainly relied on ground truth kinematics actions or expert annotations for specific tasks, limiting their applicability beyond the narrow range of tasks with ground-truth annotations. In contrast, existing works in the natural domain have shown promising results in learning latent actions solely from unlabeled videos and transferring the learned actions across contexts. We observe that this paradigm is short-sighted for the surgical domain, failing to capture the hierarchical and goal-directed structure of surgical procedures. Thus, we propose a novel multi-step action learning framework to learn context-invariant latent actions that are easily transferable across various surgical contexts. We perform extensive experiments on the publicly available datasets, SurgToolLoc-2022 and PitVis-2023, verifying our approach with both quantitative metrics and qualitative evaluation by surgical experts. We have published our code and the implementation details at this https URL.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2611.05443 [cs.CV]
(or arXiv:2611.05443v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2611.05443
arXiv-issued DOI via DataCite
Submission history
From: Love Panta [view email] [v1] Tue, 3 Nov 2026 15:59:37 UTC (22,295 KB)
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