# OphEdit: Training-Free Text-Guided Editing of Ophthalmic Surgical Videos

arXiv: https://arxiv.org/abs/2605.07695 · HTML: https://mlsanity.com/paper/2605.07695/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 13 references that are in the map.
- First posted on arXiv 8 May 2026.

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

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://mlsanity.com/paper/2412.03603/) (2024)
- [Open-Sora: Democratizing Efficient Video Production for All](https://mlsanity.com/paper/2412.20404/) (2024)
- [FateZero: Fusing Attentions for Zero-shot Text-based Video Editing](https://mlsanity.com/paper/2303.09535/) (2023)
- [FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing](https://mlsanity.com/paper/2310.05922/) (2023)
- [UniEdit: A Unified Tuning-Free Framework for Video Motion and Appearance Editing](https://mlsanity.com/paper/2402.13185/) (2024)
- [StreamDiT: Real-Time Streaming Text-to-Video Generation](https://mlsanity.com/paper/2507.03745/) (2025)
- [Ophora: A Large-Scale Data-Driven Text-Guided Ophthalmic Surgical Video Generation Model](https://mlsanity.com/paper/2505.07449/) (2025)
- [CAT-SG: A Large Dynamic Scene Graph Dataset for Fine-Grained Understanding of Cataract Surgery](https://mlsanity.com/paper/2506.21813/) (2025)
- [Surgeons vs. Computer Vision: A comparative analysis on surgical phase recognition capabilities](https://mlsanity.com/paper/2504.18954/) (2025)

## Abstract (arXiv)

High-fidelity surgical video generation can greatly improve medical training and the development of AI, adapting these generative models for precise video editing remains a formidable challenge. Modifying surgical attributes, such as instrument tissue interactions or procedural phases is challenging due to the strict anatomical and temporal constraints. In this paper, we propose OphEdit, a novel training-free framework for the text-guided editing of ophthalmic surgical videos. Our approach leverages a deterministic second-order ODE inversion pipeline to capture Attention Value (V) tensors from the original video. By selectively injecting these stored tensors into the conditional Classifier-Free Guidance (CFG) branch during the denoising phase, OphEdit rigorously preserves the intricate anatomical geometry of the eye while seamlessly mapping text-driven semantic modifications onto the video stream. Clinical evaluations demonstrates that OphEdit effectively handles complex surgical transformations, such as instrument swaps and procedural variations, with superior structural fidelity and temporal consistency compared to natural-domain video editors. Our work represents the first application of training-free video editing in the ophthalmic surgical domain, offering a scalable solution for generating diverse, annotated medical datasets without the need for exhaustive manual recording or costly model fine-tuning. The code and prompts can be accessed at https://github.com/ophedit/OphEdit