# SurReal: enhancing Surgical simulation Realism using style transfer

arXiv: https://arxiv.org/abs/1811.02946 · HTML: https://mlsanity.com/paper/1811.02946/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2018.

## Built on by (most cited first, 4 of 4)

- [Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translation](https://mlsanity.com/paper/2107.06941/) (2021)
- [Simulation-to-Real domain adaptation with teacher-student learning for endoscopic instrument segmentation](https://mlsanity.com/paper/2103.01593/) (2021)
- [Cataract-LMM Large-Scale Multi-Source Multi-Task Benchmark for Deep Learning in Surgical Video Analysis](https://mlsanity.com/paper/2510.16371/) (2025)
- [Hierarchical Curriculum Learning for Surgical Video Generation: Focusing on Bilateral Axillo-Breast Approach Robotic Thyroidectomy Datasets](https://mlsanity.com/paper/2610.17684/) (2026)

## Abstract (arXiv)

Surgical simulation is an increasingly important element of surgical education. Using simulation can be a means to address some of the significant challenges in developing surgical skills with limited time and resources. The photo-realistic fidelity of simulations is a key feature that can improve the experience and transfer ratio of trainees. In this paper, we demonstrate how we can enhance the visual fidelity of existing surgical simulation by performing style transfer of multi-class labels from real surgical video onto synthetic content. We demonstrate our approach on simulations of cataract surgery using real data labels from an existing public dataset. Our results highlight the feasibility of the approach and also the powerful possibility to extend this technique to incorporate additional temporal constraints and to different applications.