1The Chinese University of Hong Kong
2Massachusetts Institute of Technology
3Centre for Artificial Intelligence and Robotics of Hong Kong
4Shanghai Artificial Intelligence Laboratory
Generative models hold promise for revolutionizing medical education, robot-assisted surgery, and data augmentation for machine learning.
Despite progress in generating 2D medical images, the complex domain of clinical video generation has largely remained untapped.
This paper introduces Endora, an innovative approach to generate medical videos to simulate clinical endoscopy scenes.
We present a novel generative model design that integrates a meticulously crafted spatial-temporal video transformer with advanced 2D vision foundation model priors, explicitly modeling spatial-temporal dynamics during video generation.
We also pioneer the first public benchmark for endoscopy simulation with video generation models, adapting existing state-of-the-art methods for this endeavor.
Endora demonstrates exceptional visual quality in generating endoscopy videos, surpassing state-of-the-art methods in extensive testing.
Moreover, we explore how this endoscopy simulator can empower downstream video analysis tasks and even generate 3D medical scenes with multi-view consistency.
In a nutshell, Endora marks a notable breakthrough in the deployment of generative AI for clinical endoscopy research, setting a substantial stage for further advances in medical content generation.
We train a Gaussian Splatting representationon on the sampled videos by Endora
and observe the multi-view consistent geometry as if in the real 3D world.
@article{li2024endora,
author = {Chenxin Li and Hengyu Liu and Yifan Liu and Brandon Y. Feng, and Wuyang Li and Xinyu Liu, Zhen Chen and Jing shao and Yixuan Yuan},
title = {Endora: Video Generation Models as Endoscopy Simulators},
journal = {Arxiv},
year = {2024},
}
A real-time surgincal scene reconstruction framework built on 3D Gaussian Splatting