SurgVista: Long-Horizon Surgical World Modeling with Plausible Instrument-Tissue Dynamics

• URL: http://arxiv.org/abs/2606.19889v1
• Date: Thu, 18 Jun 2026 07:47:28 GMT
• Title: SurgVista: Long-Horizon Surgical World Modeling with Plausible Instrument-Tissue Dynamics
• Abstract summary: SurgVista is a surgical world model that mitigates spatial interaction incoherence and temporal fidelity collapse.<n>It consistently outperforms state-of-the-art methods across visual quality, temporal consistency, and interaction fidelity.
• Score: 49.771521708359955
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• A Physics-Grounded Benchmark for Multi-Agent Dynamics in World Models [96.64934195520802]
  We introduce CrashTwin, a physics-grounded evaluation framework designed to stress-test the physical trustworthiness of world models.<n>CrashTwin couples a diverse dataset of multi-agent collision scenarios, comprising 25K controllable synthetic 12K in-the-wild real-world collision sequences.<n>We propose a diagnostic suite that systematically evaluates three dimensions:-temporal consistency, momentum and kinetic energy conservation, and world-dynamics integrity.
  arXiv  Detail & Related papers  (2026-06-27T06:13:35Z)
• Trimming the Long-Tail of Visual World Modeling Evaluation [67.17191772079316]
  We introduce Tailor-Bench, a benchmark that challenges world models to simulate irregular physical interactions.<n>Regular scenarios reflect common tool-task pairs, Unconventional scenarios replace conventional tools with attribute-compatible substitutes, and Impossible scenarios introduce attribute-violating tools.<n>Our experimental results reveal a clear long-tail gap in physical world modeling, indicating limited generalization beyond common interactions.
  arXiv  Detail & Related papers  (2026-06-23T07:43:41Z)
• Face-D(^2)CL: Multi-Domain Synergistic Representation with Dual Continual Learning for Facial DeepFake Detection [21.09692782202372]
  Continual learning provides an effective approach to adapt facial DeepFake detection models to evolving forgery patterns.<n>We propose Face-D(2)CL, a framework for facial DeepFake detection.
  arXiv  Detail & Related papers  (2026-04-09T12:18:42Z)
• Consistency-Driven Dual LSTM Models for Kinematic Control of a Wearable Soft Robotic Arm [33.333272339511446]
  We introduce a consistency-driven dual LSTM framework for accurately learning both the forward and inverse kinematics of a pneumatically actuated soft robotic arm integrated into a wearable device.<n>This approach effectively captures the nonlinear and hysteretic behaviors of soft pneumatic actuators while addressing the one-to-many mapping challenge between actuation inputs and end-effector positions.
  arXiv  Detail & Related papers  (2026-03-18T12:51:12Z)
• FutureVLA: Joint Visuomotor Prediction for Vision-Language-Action Model [73.03346643967309]
  We argue that effective joint visuomotor predictive modeling requires both temporal continuity and visually-conditioned supervision decoupling.<n>FutureVLA is designed to extract joint visuomotor embeddings by first decoupling visual and motor information.<n>In the post-training stage, we employ a latent embeddings alignment strategy, enabling diverse downstream VLA models to internalize these temporal priors.
  arXiv  Detail & Related papers  (2026-03-11T12:39:55Z)
• STRIDE: Structured Lagrangian and Stochastic Residual Dynamics via Flow Matching [2.5845893156827158]
  We propose STRIDE, a dynamics learning framework that separates conservative rigid-body mechanics from uncertain, effectively non-conservative interaction effects.<n>We evaluate STRIDE on systems of increasing complexity, including a pendulum, the Unitree Go1ruped, and the Unitree G1 humanoid.<n>Results show 20% reduction in long-horizon prediction error and 30% reduction in contact force prediction error compared to deterministic residual baselines.
  arXiv  Detail & Related papers  (2026-03-09T15:15:21Z)
• Optimization-Guided Diffusion for Interactive Scene Generation [52.23368750264419]
  We present OMEGA, an optimization-guided, training-free framework that enforces structural consistency and interaction awareness during diffusion-based sampling.<n>We show that OMEGA improves generation realism, consistency, and controllability, increasing the ratio of physically and behaviorally valid scenes.<n>Our approach can also generate $5times$ more near-collision frames with a time-to-collision under three seconds.
  arXiv  Detail & Related papers  (2025-12-08T15:56:18Z)
• On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Driving [59.33715889581687]
  The existence of real-world adversarial examples (commonly in the form of patches) poses a serious threat for the use of deep learning models in safety-critical computer vision tasks. This paper presents an evaluation of the robustness of semantic segmentation models when attacked with different types of adversarial patches. A novel loss function is proposed to improve the capabilities of attackers in inducing a misclassification of pixels.
  arXiv  Detail & Related papers  (2022-01-05T22:33:43Z)
• Heteroscedastic Uncertainty for Robust Generative Latent Dynamics [7.107159120605662]
  We present a method to jointly learn a latent state representation and the associated dynamics. As our main contribution, we describe how our representation is able to capture a notion of heteroscedastic or input-specific uncertainty. We present results from prediction and control experiments on two image-based tasks.
  arXiv  Detail & Related papers  (2020-08-18T21:04:33Z)
• Consistency Guided Scene Flow Estimation [159.24395181068218]
  CGSF is a self-supervised framework for the joint reconstruction of 3D scene structure and motion from stereo video. We show that the proposed model can reliably predict disparity and scene flow in challenging imagery. It achieves better generalization than the state-of-the-art, and adapts quickly and robustly to unseen domains.
  arXiv  Detail & Related papers  (2020-06-19T17:28:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.