LIVE: Leveraging Image Manipulation Priors for Instruction-based Video Editing
LIVE framework combines image manipulation priors with video data, enabling multi-task instruction-based video editing with state-of-the-art performance.
Key Findings
Methodology
This study introduces LIVE, a joint training framework that leverages large-scale high-quality image editing datasets alongside video data. It employs a frame-wise token noise strategy, injecting noise into latent tokens of randomly selected frames to implicitly model temporal dynamics. The approach adopts a two-stage training process: initially, a high ratio of image data is used to instill diverse static transformations within a Diffusion Transformer (DiT), followed by increased video data to refine temporal coherence. The training is grounded in Flow Matching theory, which utilizes ODE-based loss functions to ensure stability. The model integrates cross-attention mechanisms to fuse reference video tokens and instruction embeddings, enabling flexible, instruction-guided editing across various tasks.
Key Results
- On the LIVE-Bench benchmark, our model achieves the highest overall score of 7.79 out of 9, surpassing existing methods like Lucy Edit, Ditto, and ReCo. It improves editing quality by 1.62 points (from 7.16 to 8.56) and boosts task success rate by 34.23%. The model demonstrates robust performance across diverse tasks, including style transfer, object insertion, and motion transfer, with significant improvements in temporal consistency and perceptual quality.
- In the EditVerseBench, our approach outperforms previous state-of-the-art models, with an average score increase of approximately 0.8-1.0 points across metrics such as CLIP alignment, video quality, and temporal coherence. Ablation studies confirm that the token noise strategy and two-stage training are critical for these gains, especially under limited video data conditions.
- Extensive experiments reveal that the combination of image priors and temporal modeling leads to better generalization, enabling complex multi-reference and creative editing tasks that were previously infeasible with existing datasets and models.
Significance
This work addresses the critical bottleneck of scarce large-scale video editing datasets by transferring rich static image editing priors into the video domain. It significantly broadens the scope of instruction-based editing, supporting complex, multi-task, and creative scenarios. The framework paves the way for more flexible, efficient, and high-fidelity content creation tools, with immediate applications in personalized video production, virtual avatars, and entertainment industries. Its theoretical grounding in Flow Matching also provides stability and interpretability, fostering further research in diffusion-based video synthesis.
Technical Contribution
The paper introduces a novel hybrid training paradigm that combines image and video datasets, utilizing a frame-wise token noise strategy to implicitly model temporal dynamics. It innovates by integrating a two-stage curriculum learning process, first emphasizing static transformations, then temporal coherence. The approach is based on Flow Matching theory, ensuring training stability and theoretical soundness. Architecturally, it employs a Diffusion Transformer with cross-attention modules for multi-reference and instruction fusion, enabling flexible, controllable video editing. These contributions collectively advance instruction-guided video synthesis, especially under data-limited conditions.
Novelty
This is the first work to systematically transfer large-scale image editing priors into instruction-based video editing, addressing the domain gap via frame-wise token noise. The dual-stage training strategy effectively balances static transformation learning with temporal coherence, a novel approach not seen in prior methods. It also introduces a comprehensive benchmark with over 60 challenging tasks, filling a significant gap in evaluation standards for instruction-guided video editing.
Limitations
- Despite impressive results, the model struggles with highly complex multi-reference and style fusion tasks, mainly due to limited diversity in training data for such scenarios.
- Training costs are substantial, especially for high-resolution videos, limiting real-time applications and scalability.
- Support for ultra-high-resolution videos remains limited; future work should focus on optimizing model efficiency and scalability.
Future Work
Future directions include integrating multimodal instructions for more nuanced editing, reducing computational costs for real-time applications, and extending high-resolution support. Additionally, exploring reinforcement learning to improve content consistency and creativity, as well as expanding datasets to cover more complex editing scenarios, will be key to advancing this field.
AI Executive Summary
In recent years, instruction-based video editing has emerged as a promising frontier, enabling users to modify videos through natural language commands. However, existing methods face significant challenges due to limited annotated datasets, high annotation costs, and domain gaps between static images and dynamic videos. To address these issues, this work introduces LIVE, a novel framework that leverages the rich prior knowledge embedded in large-scale image editing datasets and transfers it effectively into the video domain.
The core innovation lies in a frame-wise token noise strategy, which injects stochastic perturbations into latent representations of randomly selected frames during training. This encourages the model to implicitly learn temporal dynamics without explicit supervision. Coupled with a two-stage curriculum learning approach—first emphasizing static transformations with abundant image data, then refining temporal coherence with video data—the framework achieves a balanced understanding of content manipulation and motion consistency.
Built upon Flow Matching principles, the model employs a diffusion transformer architecture with cross-attention modules, enabling flexible, instruction-guided editing across diverse tasks such as style transfer, object insertion, and motion transfer. Extensive evaluations on the newly curated LIVE-Bench and existing benchmarks demonstrate that our approach surpasses state-of-the-art methods, achieving a 7.79 overall score and significantly improving editing quality and task success rate.
This advancement not only mitigates the data scarcity problem but also broadens the scope of instruction-based video editing, supporting complex multi-reference and creative tasks previously unattainable. Its implications extend to personalized content creation, virtual avatar animation, and entertainment industries, promising a future where high-quality, controllable, and efficient video editing becomes accessible to a wider audience. Nonetheless, challenges remain in scaling to ultra-high resolutions and reducing computational costs, which will guide future research efforts.
Deep Dive
Plain Language Accessible to non-experts
想象你在一家工厂里制作各种产品。以前，工厂只用一种简单的机器，能做一些基础的东西，但如果你想做更复杂的产品，就需要很多不同的机器和工艺。现在，这个工厂引入了一台非常聪明的机器人，它可以学习很多不同的制作方法，还能根据你的指示调整工艺。这个机器人就像论文中的模型，它通过学习大量图片和视频的制作技巧，变得非常聪明，能快速帮你制作出各种复杂的视频内容。你只要告诉它想要什么，比如换背景、加特效或者让人物动起来，它都能理解并帮你完成。它还能学会不同的风格，就像你告诉它要做成卡通风格或者写实风格一样。虽然还不能完美做到所有事情，但这个新技术让视频编辑变得像玩游戏一样简单又有趣，未来还能帮你创造出更多精彩的视频内容。
ELI14 Explained like you're 14
想象你喜欢用手机拍视频，然后想让它变得更酷，比如加点风格或者换个背景。以前，要做到这些，你可能需要用很多复杂的软件，还得花很多时间学习。现在，这个新技术就像一个超级聪明的助手，只要你告诉它想要什么，它就能帮你自动完成各种编辑，比如换背景、加特效、甚至让人物动起来。它学习了很多图片和视频的制作方法，就像你看过很多教程一样，变得非常聪明。它还能根据你的指示，做出你想要的效果，像魔法一样。虽然还不能完美解决所有问题，但它让视频编辑变得简单又有趣，就像和朋友一起玩游戏一样轻松。未来，这个助手会变得更聪明，帮你创造出更多精彩的视频内容。
Abstract
Video editing aims to modify input videos according to user intent. Recently, end-to-end training methods have garnered widespread attention, constructing paired video editing data through video generation or editing models. However, compared to image editing, the high annotation costs of video data severely constrain the scale, quality, and task diversity of video editing datasets when relying on video generative models or manual annotation. To bridge this gap, we propose LIVE, a joint training framework that leverages large-scale, high-quality image editing data alongside video datasets to bolster editing capabilities. To mitigate the domain discrepancy between static images and dynamic videos, we introduce a frame-wise token noise strategy, which treats the latents of specific frames as reasoning tokens, leveraging large pretrained video generative models to create plausible temporal transformations. Moreover, through cleaning public datasets and constructing an automated data pipeline, we adopt a two-stage training strategy to anneal video editing capabilities. Furthermore, we curate a comprehensive evaluation benchmark encompassing over 60 challenging tasks that are prevalent in image editing but scarce in existing video datasets. Extensive comparative and ablation experiments demonstrate that our method achieves state-of-the-art performance. The source code will be publicly available.