EasyV2V: A High-quality Instruction-based Video Editing Framework

AI in healthcare

Published: arXiv: 2512.16920v1

Authors

Jinjie Mai Chaoyang Wang Guocheng Gordon Qian Willi Menapace Sergey Tulyakov Bernard Ghanem Peter Wonka Ashkan Mirzaei

Abstract

While image editing has advanced rapidly, video editing remains less explored, facing challenges in consistency, control, and generalization. We study the design space of data, architecture, and control, and introduce \emph{EasyV2V}, a simple and effective framework for instruction-based video editing. On the data side, we compose existing experts with fast inverses to build diverse video pairs, lift image edit pairs into videos via single-frame supervision and pseudo pairs with shared affine motion, mine dense-captioned clips for video pairs, and add transition supervision to teach how edits unfold. On the model side, we observe that pretrained text-to-video models possess editing capability, motivating a simplified design. Simple sequence concatenation for conditioning with light LoRA fine-tuning suffices to train a strong model. For control, we unify spatiotemporal control via a single mask mechanism and support optional reference images. Overall, EasyV2V works with flexible inputs, e.g., video+text, video+mask+text, video+mask+reference+text, and achieves state-of-the-art video editing results, surpassing concurrent and commercial systems. Project page: https://snap-research.github.io/easyv2v/

Paper Summary

Problem

The main problem addressed by this research paper is the challenge of creating high-quality, instruction-based video editors that can perform a wide range of edits, such as transforming objects, changing styles, and adding effects, while maintaining control and consistency.

Key Innovation

The key innovation of this work is the introduction of EasyV2V, a lightweight and effective framework for instruction-based video editing that combines a novel data strategy with a minimal-tuning architecture. EasyV2V is designed to be flexible, allowing it to accept various types of inputs, such as video+text, video+mask+text, and video+mask+reference+text.

Practical Impact

This research has significant practical implications for the field of video editing. EasyV2V's ability to perform high-quality edits with extensive controllability makes it a valuable tool for a wide range of applications, including film and television production, advertising, and social media content creation. Additionally, EasyV2V's performance on image editing tasks suggests that it could be used as a general-purpose image editing tool, surpassing commercial systems in some cases.

Analogy / Intuitive Explanation

Imagine you're trying to create a special effect in a movie, such as turning a person into a cartoon character. Traditional video editing software would require you to manually select and manipulate each frame of the video, which can be time-consuming and labor-intensive. EasyV2V, on the other hand, is like having a magic editor that can understand your instructions and create the desired effect automatically, using a combination of artificial intelligence and machine learning algorithms to generate high-quality video output.

Paper Information

Categories:

cs.CV cs.AI

Published Date:

arXiv ID:

2512.16920v1

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