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

2025-12-18

AI Summary

Video editing lags significantly behind image editing in spatiotemporal consistency, fine-grained control, and cross-task generalization. To address these challenges, we propose the first lightweight instruction-driven video editing framework. Our method introduces: (1) a unified spatiotemporal masking mechanism enabling multimodal inputs—including video+text, mask+reference image—and precise spatiotemporal control; (2) an implicit editing paradigm built upon pretrained text-to-video diffusion models, achieving high-fidelity editing via simple sequence concatenation and lightweight LoRA fine-tuning; and (3) an end-to-end data synthesis and training strategy incorporating pseudo-video pair generation, dense caption mining, transition-aware supervision, and Affine motion modeling. Experiments demonstrate state-of-the-art performance across multiple objective and subjective metrics, surpassing both open-source and commercial baselines—achieving superior editing quality while maintaining strong spatiotemporal coherence and cross-task generalization capability.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Web Mining and Content Analysis: Large pretrained models with web data

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/

Problem

• Addresses video editing challenges in consistency, control, and generalization.
• Develops a framework for instruction-based video editing using data and model innovations.
• Enables flexible video editing with inputs like text, masks, and reference images.

Innovation

• Leverages pretrained text-to-video models with LoRA fine-tuning
• Builds diverse video training pairs via expert composition and supervision
• Unifies spatiotemporal control with a single mask mechanism