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Abstract

A surgical world model capable of generating realistic surgical action videos with precise control over tool-tissue interactions can address fundamental challenges in surgical AI and simulation – from data scarcity and rare event synthesis to bridging the sim-to-real gap for surgical automation. However, current video generation methods, the core of such surgical world models, require expensive annotations or complex structured intermediates as conditioning signals at inference, limiting their scalability. Other approaches exhibit limited temporal consistency across complex laparoscopic scenes and do not possess sufficient realism. We propose Surgical Action World (SAW) – a step toward surgical action world modeling through video diffusion conditioned on four lightweight signals: language prompts encoding tool-action context, a reference surgical scene, tissue affordance mask, and 2D tool-tip trajectories. We design a conditional video diffusion approach that reformulates video-to-video diffusion into trajectory-conditioned surgical action synthesis. The backbone diffusion model is fine-tuned on a custom-curated dataset of 12,044 laparoscopic clips with lightweight spatiotemporal conditioning signals, leveraging a depth consistency loss to enforce geometric plausibility without requiring depth at inference. SAW achieves state-of-the-art temporal consistency (CD-FVD: 199.19 vs. 546.82) and strong visual quality on held-out test data. Furthermore, we demonstrate its downstream utility for (a) surgical AI, where augmenting rare actions with SAW-generated videos improves action recognition (clipping F1-score: 20.93% to 43.14%; cutting: 0.00% to 8.33%) on real test data, and (b) surgical simulation, where rendering tool-tissue interaction videos from simulator-derived trajectory points toward a visually faithful simulation engine.

Links to Paper and Supplementary Materials

Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026/paper/2781_paper.pdf

SharedIt Link: Not yet available

SpringerLink (DOI): Not yet available

Supplementary Material: https://papers.miccai.org/miccai-2026/supp/2781_supp.zip

Link to the Code Repository

N/A

Link to the Dataset(s)

N/A

BibTex

@InProceedings{RapSam_SAW_MICCAI2026,
        author = { Rapuri, Sampath AND Seenivasan, Lalithkumar AND Schneider, Dominik AND Soberanis-Mukul, Roger AND He, Yufan AND Ding, Hao AND Xu, Jiru AND Yu, Chenhao AND Jing, Chenyan AND Guo, Pengfei AND Xu, Daguang AND Unberath, Mathias},
        title = { { SAW: Toward a Surgical Action World Model via Controllable and Scalable Video Generation } },
        booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026},
        year = {2026},
        publisher = {Springer Nature Switzerland},
        volume = {LNCS 16893},
        month = {September},
        page = {pending}
}

Reviews

Review #1

  • Please describe the contribution of the paper

    The paper presents SAW (Surgical Action World), a method for generating surgical videos conditioned on specific inputs such as language prompts, tool-tip trajectories, tissue affordance maps, and a reference frame. The topic is interesting and potentially useful for applications such as data augmentation.

  • Please list the major strengths of the paper: you should highlight a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.

    The authors provided a model for the generation of video surpassing all previous efforts. Although the model is built on already available networks their combination is smart. The authors further evaluate the model on downstream experiment demonstrating potential practical utility, which is very important.

  • Please list the major weaknesses of the paper. Please provide details: for instance, if you state that a formulation, way of using data, demonstration of clinical feasibility, or application is not novel, then you must provide specific references to prior work.

    Major concerns:

    • My main concern is the use of the term “world model. ” In its standard meaning, a world model predicts future states conditioned on actions, for example in a state-action-next state formulation. In surgical settings, this would typically correspond to predicting future observations based on control inputs or actions sent to the end-effectors. In contrast, SAW appears to be a controllable video generation method guided by prompts, tool-tip trajectories, and tissue affordance, rather than a model of environment dynamics. For this reason, I believe the method is better described as a conditional surgical video generation framework than as a world model. A relevant prior work in this context is Surgical Vision World Model, previously published at MICCAI last year: https://arxiv. org/pdf/2503.02904.That work is more closely aligned with the world model formulation, and I think the distinction should be made clearer here. As written, the current name and framing may be misleading. Please change name and title.
    • Related to the previous point, the statement in the conclusion that this is a “first step toward surgical action world modeling” is difficult to justify. There is already prior work on surgical world models, and this literature is not sufficiently discussed. In particular, the survey “Deep Reinforcement Learning for Surgical Robotics with State and Image Information: A Survey” (https://assets-eu. researchsquare. com/files/rs-8621244/v1_covered_543c3a07-ebe7-4ecc-bbb5-0de157192396.pdf?c=1769506908) describes the current state of the art for surgical world models and related architectures, but it is not cited. I think the paper would benefit from a clearer positioning with respect to these existing works.
    • I also have concerns about the use and interpretation of the evaluation metrics. The paper states that “CD-FVD is sensitive to temporal inconsistencies,” making it relevant for evaluating the temporal consistency of tool-tissue interactions. While this may be true, no reference is provided for this claim, and the paper does not include an analysis showing that CD-FVD indeed reflects tool-tissue interaction quality in this setting. As a result, the relevance of this metric to the specific surgical application remains assumed rather than demonstrated.
    • More generally, the evaluation relies heavily on perceptual metrics such as FVD and CD-FVD, without validating their relationship to surgical realism or tool-tissue interaction fidelity. In my view, this could have been addressed with a relatively small expert evaluation, for example by asking 2–5 surgeons or trained residents to rate short generated clips with respect to tool motion realism, tissue response realism, and plausibility of tool-tissue contact. Such an evaluation would make the conclusions more convincing.
    • Another concern is the claim about the depth consistency loss. The paper states that it introduces a “novel depth consistency loss enforcing geometric plausibility without requiring depth at inference. ” However, the ablation results in Table 2 seem to complicate this interpretation. In particular, the model trained without the depth consistency loss achieves the best FVD and LPIPS values, while CD-FVD is only slightly worse than the full model. This makes the practical benefit of the loss less clear than suggested in the text. I think this part needs a more careful discussion, especially since the current results do not fully support the strength of the claim.

    Minor: there is a small formatting issue in “12, 044” which should be “12,044” in the abstract.

  • Please rate the clarity and organization of this paper

    Satisfactory

  • Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.

    The submission does not mention open access to source code or data, but provides a clear and detailed description of the algorithm to ensure reproducibility.

  • Based on your review and your understanding of the MICCAI Scientific Code of Ethics, do you believe this submission may involve a potential ethics concern or violation?

    N/A

  • Optional: If you have any additional comments to share with the authors, please provide them here. Please also refer to our Reviewer’s guide on what makes a good review and pay specific attention to the different assessment criteria for the different paper categories: https://conferences.miccai.org/2026/en/REVIEWER-GUIDELINES.html

    N/A

  • Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making.

    (3) Weak Reject — marginally below the acceptance threshold, but would not mind if accepted, dependent on rebuttal

  • Please justify your recommendation. What were the major factors that led you to your overall score for this paper?

    I have several concerns regarding the framing of the contribution, the positioning with respect to prior work, and some aspects of the evaluation as indicated in the comments.

  • Reviewer confidence

    Very confident (4)

  • [Post rebuttal] After reading the authors’ rebuttal, please state your final opinion of the paper.

    Accept

  • [Post rebuttal] Please justify your final decision from above.

    The authors covered all my concerns making the paper stronger and suitable for acceptance.



Review #2

  • Please describe the contribution of the paper

    This study presents a new video generation method as a surgical world action model. The contribution consists of: 1) a dataset comprising 12044 endoscopic sequences. 2) a novel video generation model conditioned on 4 inputs. 3) downstream applications experiments to validate the effectiveness of the proposed surgical world model.

  • Please list the major strengths of the paper: you should highlight a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.

    1) The authors collect and curate a dataset from the internet and other open-source datasets. The relabeled information includes text narration, tool tip trajectory, and tissue affordance mask. 2) Explore the fintuned LTX-video’s performance on the surgical scene. Compared with a previous work, SurgSora, the proposed method achieves better metrics.

  • Please list the major weaknesses of the paper. Please provide details: for instance, if you state that a formulation, way of using data, demonstration of clinical feasibility, or application is not novel, then you must provide specific references to prior work.

    Below are some unclear points and concerns: 1) In Fig. 1 (a), why are the LoRA layers learnable during the inference stage? Similarly, in Fig (b), it is not clear which component is updated and what the loss function is. In Fig. 1 (c), the smooth L1 loss is not mentioned in the caption, and the depth consistency loss (L_DC) is not in the figure. In general, Fig. 1 is confusing to me. I recommend modifying it. 2) In Section 2.1, the details of the In-Context Low Rank Adaptation (IC-LoRA) are missing. If you borrow IC-LoRA from previous work, please cite it. If it is a novel strategy, please clarify in detail. 3) The results of ablation experiments are not convincing. The proposed method is the best in only 1 out of 5 ablations. 4) The tool tip trajectory signal is not explained properly. Why and how to encode instrument classes through the R and G channels? 5) In Section 2.3, the authors claim that depth consistency loss can enforce geometric consistency. However, this statement is not validated in the following experiment section. The metrics in the experiment can not reflect good geometric consistency. 6) In addition, using depth loss in video generation is not novel. Previous work has already presented similar loss function. For example: Bai, Y., Fang, S., Yu, C., Wang, F. and Huang, Q., 2025.Geovideo: Introducing geometric regularization into video generation model. arXiv preprint arXiv:2512.03453.There is no citation of related work and no discussion of the differences. 7) Two downstream application experiments are not solid. For the action recognition task, the proposed method is optimal in only 5 out of 8 trials. For the realistic tool-tissue interaction engine, the attached video is not sufficient to demonstrate its advancement.

  • Please rate the clarity and organization of this paper

    Satisfactory

  • Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.

    The submission does not mention open access to source code or data, but provides a clear and detailed description of the algorithm to ensure reproducibility.

  • Based on your review and your understanding of the MICCAI Scientific Code of Ethics, do you believe this submission may involve a potential ethics concern or violation?

    N/A

  • Optional: If you have any additional comments to share with the authors, please provide them here. Please also refer to our Reviewer’s guide on what makes a good review and pay specific attention to the different assessment criteria for the different paper categories: https://conferences.miccai.org/2026/en/REVIEWER-GUIDELINES.html

    N/A

  • Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making.

    (3) Weak Reject — marginally below the acceptance threshold, but would not mind if accepted, dependent on rebuttal

  • Please justify your recommendation. What were the major factors that led you to your overall score for this paper?

    From the perspective of methodology, this paper does not clearly clarify its novelty. From the experimental perspective, this study does not provide sufficient proof of its superior performance. Thus, I suggest Weak Reject.

  • Reviewer confidence

    Confident but not absolutely certain (3)

  • [Post rebuttal] After reading the authors’ rebuttal, please state your final opinion of the paper.

    Reject

  • [Post rebuttal] Please justify your final decision from above.

    I still am not convinced because: 1) Depth Consistency Loss: Yes, I am aware that L1 smooth loss is adopted in this work. But my question is: can this be counted as one of the main contributions? Introducing depth loss is not a novel idea. And why, during the training, is the depth from an off-the-shelf model needed but not during the inference? 2) 2D Tooltip trajectory condition: In the real world, the tool tip trajectory is 3D. The 2D trajectory condition is probably not the optimal strategy. In addition, I do not understand the clarification about the R/G channel confusion. 3) Downstream application: The author does not explain my concern about the realistic tool-tissue interaction engine using this work.



Review #3

  • Please describe the contribution of the paper

    The paper presents the Surgical Action World (SAW) model, which leverages video diffusion conditioned on lightweight signals to generate realistic and controllable surgical action videos. This model addresses key challenges in surgical AI and simulation, including data scarcity and rare event synthesis. The approach achieves strong performance in generating temporally consistent and visually high-quality videos, validated on a reasonable downstream task.

  • Please list the major strengths of the paper: you should highlight a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.

    Well-curated dataset (12,044 laparoscopic clips). Strong performance in terms of visual quality and temporal consistency, outperforming compared state-of-the-art method SurgSora. Two useful downstream applications: improving action recognition on real test data and rendering realistic tool-tissue interactions for surgical simulation.

  • Please list the major weaknesses of the paper. Please provide details: for instance, if you state that a formulation, way of using data, demonstration of clinical feasibility, or application is not novel, then you must provide specific references to prior work.

    Somewhat limited methodological novelty—the paper adapts existing method LTX using LORA and applies additional Depth Consistency loss. Neither of these methodological components doesn’t introduce a fundamentally new approach from my perspective. The evaluation is based on only one fine-tuned baseline, which could be considered a limitation when comparing performance against the state of the art. If I understand correctly, the method requires the presence of all signals at the same time? this might limit its applicability and could be clarified further (perhaps in Table 1). It’s unclear to me if downstream task would benefit more from this dedicated SAW model than from with off-the-shelf models.

  • Please rate the clarity and organization of this paper

    Good

  • Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.

    The submission does not provide sufficient information for reproducibility.

  • Based on your review and your understanding of the MICCAI Scientific Code of Ethics, do you believe this submission may involve a potential ethics concern or violation?

    N/A

  • Optional: If you have any additional comments to share with the authors, please provide them here. Please also refer to our Reviewer’s guide on what makes a good review and pay specific attention to the different assessment criteria for the different paper categories: https://conferences.miccai.org/2026/en/REVIEWER-GUIDELINES.html

    Is SurgSora the closest existing method (in terms of methodolody, conditioning signals etc?) It’s unclear to me if downstream task would benefit more from this dedicated SAW model than from with off-the-shelf models.

  • Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making.

    (4) Weak Accept — marginally above the acceptance threshold, but would not mind if rejected, dependent on rebuttal

  • Please justify your recommendation. What were the major factors that led you to your overall score for this paper?

    I see value in this work and recognize its usefulness for the community, especially together with the well-curated dataset. However, the novelty feels somewhat limited, and the evaluation is limited. I ‘d like to read author’s explanations to my concerns during rebuttal.

  • Reviewer confidence

    Confident but not absolutely certain (3)

  • [Post rebuttal] After reading the authors’ rebuttal, please state your final opinion of the paper.

    Accept

  • [Post rebuttal] Please justify your final decision from above.

    For me this paper marginally meets the bar for acceptance. However I agree with remarks from other reviewers regarding positioning (world model can be misleading although authors referred to it in rebuttal), and regarding metrics vs realism evaluation



Author Feedback

We thank the reviewers for acknowledging our strengths: Strong performance [R1,R2], with both visual quality & temporal consistency outperforming SOTA models [R2,R3]; novel approach & practical utility [R1]; grounded evaluations across tasks that are highly relevant for surgical AI﹣action recognition & realistic tool-tissue interaction [R1,R2,R3]; and a large-scale dataset (12k segmentations) with tool-tip trajectories, tissue affordance masks & instrument labels [R2,R3], and for feedback.

World Model (WM) Definition & Clarity on Conditioning Signals [R1]: While the WM term has historically been associated with state-transition predictors in Reinforcement Learning (RL), we adopt the modern definition of Generative WMs (e.g., [Sora, Genie, SurgVisionWM]). In this context, SAW treats high-dimensional video as the state space and conditions transitions on one or more conditioning (action) signal﹣text prompt, first-frame input, tissue affordance, 2D tool-tip movements, thereby modeling the complex dynamics of the surgical environment. We will clarify this point and cite parallel prior WMs (e.g., SurgVisionWM) in the final version.

Depth Consistency (DC) Loss Significance & Clarity [R1,R2,R3]: Paired bootstrap significance test on our ablation results confirms that the DC loss significantly improves CD-FVD (199.19 vs 207.59, p<0.05), a metric more sensitive to temporal inconsistency (Ge et al. cited [5]). This shows the importance of DC loss in our work in achieving realistic tool and tissue movement. Unlike GeoVideo’s global 3D reprojection, SAW applies a local Smooth-L1 term that reconstructs depth from RGB video tokens, improving the realism of tool-tissue movements without requiring depth at inference. We will better describe the DC loss in the revised Fig. 1 (currently 1c).

Methodological Novelty [R2,R3]: Our novelty lies in problem formulation and how we extend prior architectures and training strategies to achieve the first highly controllable and scalable surgical action WM. Previous works require dense, expensive signals (segmentation maps, cited [1, 15, 3]). Moreover, we integrate a depth consistency loss that improves tool-tissue interaction quality without requiring depth at inference.

Geometric Consistency & Metric Validation [R1,R2]: Because perceptual-level metrics cannot directly assess the accuracy of tool–tissue interactions, we leverage CD-FVD, which is sensitive to temporal inconsistencies (Ge et al. cited [5]). This implicitly evaluates irregular tool-tissue motion. Additionally, our downstream action-recognition model, trained on SAW-generated data, shows improved performance on real test data, highlighting the realism of the generated tool-tissue interactions. Expert rating of SAW-generated videos will be included in our future work.

Downstream task results [R2,R3]: Thank you for highlighting this. SAW-generated data only improves 5/8 actions, because we only augment underrepresented actions (cutting & clipping). Among these, 3/4 show drastic improvements (F1), with a marginal decrease in the fourth. There is also no comparable off-the-shelf model to ours in synthesizing highly realistic action videos. We outperform the closest model (SurgSora) across the major metrics of interest.

Representation of Tool-Tip Trajectory [R2]: Representing the tool-tip trajectory through the R/G channels was a careful design choice to reformulate a video-to-video diffusion to a world model. This conditioning input can thus be easily extended to synthesize action videos from a simple sketch of the tool path by the user. IC-LoRA clarity [R2]: IC-LoRA is a parameter-efficient finetuning strategy that allows for frame-by-frame control with a conditioning video. We will include its description and cite it (In-Context LoRA for Diffusion Transformers, Huang et al.)

Fig 1 clarity [R2]: We will revise the fig to include each module label & training state (frozen vs. trainable).



Meta-Review

Meta-review #1

  • Your recommendation

    Invite for Rebuttal

  • Please justify your decision. In case you deviate from the reviewers’ recommendations, explain in detail the reasons why. In case of an invitation for rebuttal, clarify which points are important to address in the rebuttal.

    This paper was reviewed by three experts in the field and received mixed evaluations: one Weak Accept and two Weak Rejects. While the reviewers noted promising downstream applications and performance, they also raised several concerns, including potentially limited novelty and evaluation, an unclear effect of the depth consistency loss, and unclear presentation. The authors are encouraged to address these concerns in the rebuttal.

  • After you have reviewed the rebuttal and updated reviews, please provide your recommendation based on all reviews and the authors’ rebuttal.

    Accept

  • Please justify your recommendation.

    After the rebuttal, two reviewers recommended acceptance, while one reviewer maintained a Reject recommendation. While the paper has some limitations regarding downstream applications, it also demonstrates several strengths, including strong performance and comprehensive evaluation. Therefore, the paper is recommended for acceptance to MICCAI 2026.The authors are encouraged to incorporate the necessary changes discussed during the rebuttal process into the final camera-ready version.



Meta-review #2

  • After you have reviewed the rebuttal and updated reviews, please provide your recommendation based on all reviews and the authors’ rebuttal.

    Accept

  • Please justify your recommendation.

    Two reviewers (R1 and R3) suggests acceptance post-rebuttal.



Meta-review #3

  • After you have reviewed the rebuttal and updated reviews, please provide your recommendation based on all reviews and the authors’ rebuttal.

    Accept

  • Please justify your recommendation.

    The rebuttal addresses several key concerns well: it clarifies the “world model” definition and positioning, provides justification (with significance testing) for the depth consistency loss, and explains design choices such as trajectory encoding and downstream results. These responses resolve major clarity and positioning issues for one reviewer. However, some concerns remain: novelty is still somewhat limited (extension of existing methods), evaluation still relies largely on perceptual metrics, and downstream validation is not fully convincing across all cases. These issues are only partially addressed and cannot be fully resolved by explanation alone. Overall, after rebuttal there is reviewers generally lean toward acceptance.



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