# MeshBrush: Painting the Anatomical Mesh with Neural Stylization for Endoscopy

arXiv: https://arxiv.org/abs/2404.02999 · HTML: https://mlsanity.com/paper/2404.02999/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 13 of 13)

- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [FlowVid: Taming Imperfect Optical Flows for Consistent Video-to-Video Synthesis](https://mlsanity.com/paper/2312.17681/) (2023)
- [StyleMesh: Style Transfer for Indoor 3D Scene Reconstructions](https://mlsanity.com/paper/2112.01530/) (2021)
- [Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation](https://mlsanity.com/paper/1907.02882/) (2019)
- [X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization via Dynamic Textual Guidance](https://mlsanity.com/paper/2303.15764/) (2023)
- [Text and Image Guided 3D Avatar Generation and Manipulation](https://mlsanity.com/paper/2202.06079/) (2022)
- [Long-Term Temporally Consistent Unpaired Video Translation from Simulated Surgical 3D Data](https://mlsanity.com/paper/2103.17204/) (2021)
- [Video ControlNet: Towards Temporally Consistent Synthetic-to-Real Video Translation Using Conditional Image Diffusion Models](https://mlsanity.com/paper/2305.19193/) (2023)
- [Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translation](https://mlsanity.com/paper/2107.06941/) (2021)
- [Improving Surgical Training Phantoms by Hyperrealism: Deep Unpaired Image-to-Image Translation from Real Surgeries](https://mlsanity.com/paper/1806.03627/) (2018)

## Built on by (most cited first, 2 of 2)

- [EndoPBR: Material and Lighting Estimation for Photorealistic Surgical Simulations via Physically-based Rendering](https://mlsanity.com/paper/2502.20669/) (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)

Style transfer is a promising approach to close the sim-to-real gap in medical endoscopy. Rendering synthetic endoscopic videos by traversing pre-operative scans (such as MRI or CT) can generate structurally accurate simulations as well as ground truth camera poses and depth maps. Although image-to-image (I2I) translation models such as CycleGAN can imitate realistic endoscopic images from these simulations, they are unsuitable for video-to-video synthesis due to the lack of temporal consistency, resulting in artifacts between frames. We propose MeshBrush, a neural mesh stylization method to synthesize temporally consistent videos with differentiable rendering. MeshBrush uses the underlying geometry of patient imaging data while leveraging existing I2I methods. With learned per-vertex textures, the stylized mesh guarantees consistency while producing high-fidelity outputs. We demonstrate that mesh stylization is a promising approach for creating realistic simulations for downstream tasks such as training networks and preoperative planning. Although our method is tested and designed for ureteroscopy, its components are transferable to general endoscopic and laparoscopic procedures. The code will be made public on GitHub.