Endora: Video Generation Models as Endoscopy Simulators

TL;DR

Endora introduces a novel generative model for creating realistic clinical endoscopy videos, combining a spatial-temporal transformer with vision priors, and establishes a new benchmark for endoscopy simulation.

Contribution

The paper presents the first endoscopy video generation model integrating a spatial-temporal transformer and vision priors, along with a public benchmark for evaluation.

Findings

Outperforms existing methods in visual quality

Enables downstream video analysis tasks

Supports 3D scene generation with multi-view consistency

Abstract

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 \model, an innovative approach to generate medical videos that 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. For more details, please visit our project page: https://endora-medvidgen.github.io/.