# Surg-UniWorld: A Unified Surgical World Model with Multimodal Control Experts

arXiv: https://arxiv.org/abs/2608.06770 · HTML: https://mlsanity.com/paper/2608.06770/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical and surgical world models, one of 11 directions in World models.
- Builds on 22 of its 22 references that are in the map.
- First posted on arXiv 7 Aug 2026.

## Placed in

- World models / [Medical and surgical world models](https://mlsanity.com/wm/medical-and-surgical-world-models/)

## Builds on (most cited first, 22 of 22)

- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [WorldSimBench: Towards Video Generation Models as World Simulators](https://mlsanity.com/paper/2410.18072/) (2024)
- [Depth Anything 3: Recovering the Visual Space from Any Views](https://mlsanity.com/paper/2511.10647/) (2025)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [LTX-Video: Realtime Video Latent Diffusion](https://mlsanity.com/paper/2501.00103/) (2024)
- [VACE: All-in-One Video Creation and Editing](https://mlsanity.com/paper/2503.07598/) (2025)
- [World Simulation with Video Foundation Models for Physical AI](https://mlsanity.com/paper/2511.00062/) (2025)
- [Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control](https://mlsanity.com/paper/2503.14492/) (2025)
- [Endora: Video Generation Models as Endoscopy Simulators](https://mlsanity.com/paper/2403.11050/) (2024)
- [WAFT: Warping-Alone Field Transforms for Optical Flow](https://mlsanity.com/paper/2506.21526/) (2025)
- [Sim-To-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgery](https://mlsanity.com/paper/2406.06092/) (2024)
- [SurGen: Text-Guided Diffusion Model for Surgical Video Generation](https://mlsanity.com/paper/2408.14028/) (2024)
- [SurgSora: Object-Aware Diffusion Model for Controllable Surgical Video Generation](https://mlsanity.com/paper/2412.14018/) (2024)
- [Future Frame Prediction for Robot-assisted Surgery](https://mlsanity.com/paper/2103.10308/) (2021)
- [How Far Are Surgeons from Surgical World Models? A Pilot Study on Zero-shot Surgical Video Generation with Expert Assessment](https://mlsanity.com/paper/2511.01775/) (2025)
- [Cosmos-H-Surgical: Learning Surgical Robot Policies from Videos via World Modeling](https://mlsanity.com/paper/2512.23162/) (2025)
- [Surgical Vision World Model](https://mlsanity.com/paper/2503.02904/) (2025)
- [Ophora: A Large-Scale Data-Driven Text-Guided Ophthalmic Surgical Video Generation Model](https://mlsanity.com/paper/2505.07449/) (2025)
- [SAW: Toward a Surgical Action World Model via Controllable and Scalable Video Generation](https://mlsanity.com/paper/2603.13024/) (2026)
- [HieraSurg: Hierarchy-Aware Diffusion Model for Surgical Video Generation](https://mlsanity.com/paper/2506.21287/) (2025)

## Abstract (arXiv)

Controllable surgical world models can provide a generative foundation for surgical artificial intelligence and simulation by synthesizing realistic instrument--tissue interactions. However, existing methods lack a unified multimodal control paradigm, while direct fusion of heterogeneous visual conditions often causes anatomical distortion, instrument appearance drift, and temporally inconsistent interactions. In this work, we propose {Surg-UniWorld}, a unified surgical world model with multimodal control experts. Surg-UniWorld first constructs a {Hierarchical Surgical Anchor} from first-frame appearance and hierarchical semantic masks to preserve persistent scene identity, anatomical organization, and interaction boundaries. {Anchor-Relative Modality Experts} then interpret edge, depth, and optical-flow evidence relative to the shared anchor, capturing complementary boundary, geometric, and motion information. A {Multimodal Control Expert} further performs contribution-preserving stage-wise composition of the activated modality increments and generates control hints for the Wan2.2 video diffusion backbone. To support multimodal surgical world modeling, we further construct Cholec80-SurgWAM, a benchmark for controllable surgical video generation. Extensive experiments demonstrate that Surg-UniWorld consistently outperforms existing controllable video generation methods and surgical world-model baselines in generation quality, temporal consistency, and multimodal controllability.