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AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks

Max Ku, Cong Wei, Weiming Ren, Harry Yang, Wenhu Chen

arXiv:2403.14468v4cs.CVcs.AIcs.MM

TL;DR

Video editing remains limited by insufficient quality and control, while prior approaches require zero-shot adaptation or fine-tuning and often rely on ambiguous textual guidance. AnyV2V uses off-the-shelf image editing for the first frame and an I2V model conditioned on source-video information to generate edited videos. It supports broader editing tasks and achieves stronger human-evaluation results than baselines, with limitations from inaccurate image edits and weak tracking of fast or complex motion.

  • Problem

    Video editing lacks the quality and control of image generation, while paired data and computational demands make large-scale video-editing training challenging.

  • Method

    AnyV2V edits a source video's first frame with an image-editing model, then conditions an I2V model with the edited frame, source-video features, and inverted latents.

  • Results

    AnyV2V supports editing tasks beyond existing methods and is preferred in 46.2% of human evaluations overall versus 20.7% for the best baseline.

  • Takeaways & Limitations

    The framework provides a training-free and compatible interface that can combine image-editing models with I2V generation for diverse video-editing tasks.

  • Takeaways & Limitations

    Image-editing models may produce inaccurate initial edits, and the method may not follow source-video motion when it is fast or complex.

Abstract

from arXiv · show

In the dynamic field of digital content creation using generative models, state-of-the-art video editing models still do not offer the level of quality and control that users desire. Previous works on video editing either extended from image-based generative models in a zero-shot manner or necessitated extensive fine-tuning, which can hinder the production of fluid video edits. Furthermore, these methods frequently rely on textual input as the editing guidance, leading to ambiguities and limiting the types of edits they can perform. Recognizing these challenges, we introduce AnyV2V, a novel tuning-free paradigm designed to simplify video editing into two primary steps: (1) employing an off-the-shelf image editing model to modify the first frame, (2) utilizing an existing image-to-video generation model to generate the edited video through temporal feature injection. AnyV2V can leverage any existing image editing tools to support an extensive array of video editing tasks, including prompt-based editing, reference-based style transfer, subject-driven editing, and identity manipulation, which were unattainable by previous methods. AnyV2V can also support any video length. Our evaluation shows that AnyV2V achieved CLIP-scores comparable to other baseline methods. Furthermore, AnyV2V significantly outperformed these baselines in human evaluations, demonstrating notable improvements in visual consistency with the source video while producing high-quality edits across all editing tasks.

1 Introduction

AnyV2V addresses limited quality and control in video editing with a tuning-free framework that separates first-frame image editing from image-to-video generation. It supports diverse editing tasks and reports stronger human-evaluation results than baselines while maintaining competitive video metrics.

  • AnyV2V Framework: AnyV2V decomposes video editing into first-frame image editing followed by image-to-video generation using the edited frame, source-video latent, and temporal features.The framework uses off-the-shelf image editing models and existing I2V models rather than fine-tuning.
  • AnyV2V Framework: AnyV2V is designed to avoid fine-tuning while achieving high appearance and temporal consistency for video editing tasks.The approach also avoids requiring additional video features used by prior works.
  • Capabilities: AnyV2V supports prompt-based editing, reference-based style transfer, subject-driven editing, and identity manipulation beyond the scope of existing publicly available methods.Its interface can integrate image editing methods across modalities.
  • Evaluation: AnyV2V is reported as superior to existing SOTA methods in quantitative and qualitative evaluations, with 69.7% prompt-alignment preference and 46.2% overall human preference versus 31.7% and 20.7% for the best baseline.It also reaches a CLIP-Text score of 0.2932 and a CLIP-Image score of 0.9652.
  • Capabilities: AnyV2V supports editing videos longer than the training frame lengths of I2V models by inverting the videos.The inverted latents enable the I2V model to produce longer edited videos.

2 Related Works

Prior video-editing methods use zero-shot adaptation or fine-tuned motion modules, but their user control and editing-task coverage remain limited. AnyV2V is presented as more applicable and compatible across editing tasks.

  • Prior Approaches: Existing video-editing approaches are categorized as zero-shot adaptation from pretrained T2I models or fine-tuned motion modules from T2I or T2V models.
  • Limitations: Prior methods often fail to provide precise user control because natural-language ambiguity and model constraints can prevent edits from matching users’ intended detail.VideoP2P, for example, is restricted to word-swapping prompts because it relies on cross-attention.
  • Comparison: Table 1 compares video-editing methods by the editing tasks they support, with AnyV2V described as excelling in applicability and compatibility.

3 Preliminary

I2V models denoise video latents conditioned on a first frame and prompt, while self-attention operates over spatial or temporal tokens. PnP-style editing preserves source structure by injecting intermediate convolution and attention features during denoising.

  • I2V models recover less noisy video latents from noisy latents using a denoising model conditioned on the first frame and text prompt.
  • Self-attention projects hidden states into query, key, and value vectors, with spatial attention spanning frame-local tokens and temporal attention spanning matching positions across frames.
  • Plug-and-Play Diffusion Features: PnP diffusion editing inverts the source image, collects convolution and attention features during reverse diffusion, and injects them while denoising the edited image.
  • Plug-and-Play Diffusion Features: AnyV2V extends PnP feature injection to I2V models by extracting spatial convolution, spatial-attention, and temporal-attention features before sampling with the edited first frame.

4 AnyV2V

AnyV2V edits a video’s first frame with a compatible image-editing model, then uses DDIM-inverted source latents and feature injection to generate a consistent edited video. Spatial and temporal injections jointly preserve source structure, appearance, and motion without tuning.

  • AnyV2V: AnyV2V first edits the source video’s initial frame with an image-editing model, then generates the video with an I2V model conditioned on the edited frame, source latent, and target prompt.
  • Flexible First Frame Editing: The framework supports diverse image-editing modalities, including style transfer, mask-based editing, inpainting, identity-preserving editing, and subject-driven editing.
  • Structural Guidance using DDIM Inversion: DDIM inversion obtains source-video latent noise without text conditioning but with the first-frame condition, while starting from an earlier timestep can avoid distortions in some I2V models.
  • Appearance and Motion Guidance: Spatial feature injection improves background and structural consistency, but source-motion errors remain, motivating temporal attention injection during video generation.
  • Putting it Together: AnyV2V combines convolution, spatial-attention, and temporal-attention feature replacement during denoising to enable tuning-free adaptation of I2V models for video editing.

5 Experiments

Experiments evaluate AnyV2V with multiple off-the-shelf image-editing and I2V models across prompt-based and reference-driven video-editing tasks. The method supports diverse edits, preserves source appearance and motion, and outperforms baselines in reported human evaluations.

  • Prompt-based Editing: AnyV2V with InstructPix2Pix precisely edits requested regions while preserving the original background and video fidelity, unlike baselines that often alter unspecified content.The I2V models also animate added motion such as snowfall without the flickering observed in baseline methods.
  • Style Transfer, Subject-Driven Editing and Identity Manipulation: AnyV2V supports reference-based style transfer, subject-driven editing, and identity manipulation through image-editing models, including styles and subjects not specified by text.Examples include matching Kandinsky and Van Gogh artworks, replacing a cat with a dog while preserving motion and background, and swapping a person’s identity.
  • I2V Backbones: I2VGen-XL was the most robust backbone, while ConsistI2V sometimes introduced watermarks and SEINE generalized less reliably despite producing consistent video for simple motion.The comparison covered I2VGen-XL, ConsistI2V, and SEINE.
  • Ablation Analysis: Temporal feature injection preserves source motion, spatial feature injection preserves appearance and layout, and DDIM-inverted noise provides structural guidance during editing.Removing these components caused motion misalignment, appearance and background degradation, or lower CLIP-Image scores and poorer visual quality.

6 Conclusion

The conclusion presents AnyV2V as a training-free, cost-effective framework that combines first-frame image editing with I2V generation for video editing at arbitrary length. Experiments report strong results across diverse applications, common video metrics, and human evaluation.

  • 6 Conclusion: AnyV2V first edits the source video’s initial frame, then conditions an I2V model with the edited frame, source-video features, and inverted latents to generate the edited video.The framework is presented as applicable to any image-editing and I2V generation model.
  • 6 Conclusion: AnyV2V achieves strong results across applications beyond existing state-of-the-art methods while obtaining superior common video metrics and human-evaluation results.

A Discussion on Model Implementation Details

The implementation analysis examines decoder-layer selection and diffusion-step thresholds for convolution, spatial-attention, and temporal-attention feature injection. It uses feature visualizations to assign layout guidance to earlier layers and detailed appearance guidance to deeper layers.

  • Hyperparameter Design: AnyV2V’s implementation depends on selecting U-Net decoder layers and injection thresholds for convolution, spatial-attention, and temporal-attention features.These hyperparameters determine where and when feature injection occurs during diffusion sampling.
  • Feature Visualization: Earlier U-Net decoder layers represent overall frame layout, whereas deeper layers capture high-frequency details such as edges and textures.The analysis visualizes average convolution activations and attention scores across candidate I2V models.
  • Feature Visualization: The method sets l1 = 4 for convolution feature injection to provide background and layout guidance without introducing excessive high-frequency details.

A.2 Ablation Analysis on Feature Injection Thresholds

The threshold ablations show that spatial and temporal injection thresholds govern the trade-off between source-video adherence, unwanted detail transfer, motion alignment, and fidelity. The selected settings are τconv = τsa = 0.2T and τta = 0.5T.

  • Effect of Spatial Injection Thresholds: τconv = τsa = 0.2T achieves the desired spatial-injection outcome, whereas disabling spatial injection harms source layout and motion adherence.Higher thresholds transfer unwanted high-frequency source details into the edited video.
  • Effect of Temporal Injection Threshold: τta = 0.5T balances motion alignment, motion consistency, and video fidelity, while lower values weaken motion guidance and higher values introduce distortion.Here, T denotes the total number of denoising steps.
  • Feature Visualization: The feature visualizations describe convolution, spatial-attention, and temporal-attention features during I2V sampling and support interpreting these threshold choices.

B.1 Quantitative Evaluations

AnyV2V is evaluated against baselines on prompt-based editing and novel reference-driven tasks using human and automatic measures. It achieves the strongest reported human-evaluation results across these settings.

  • Prompt-based Editing: AnyV2V (I2VGen-XL) achieves the best overall preference and prompt alignment among Tune-A-Video, TokenFlow, and FLATTEN.The human evaluation compares prompt-based edited videos across three baseline models.
  • Prompt-based Editing: Automatic evaluation measures CLIP-based text alignment and temporal consistency across edited-video frames.Text alignment uses prompt-to-frame cosine similarity, while temporal consistency uses image-embedding similarity across frames.
  • Reference-based Style Transfer; Identity Manipulation and Subject-driven Editing: AnyV2V (I2VGen-XL) is the best model across reference-based style transfer, identity manipulation, and subject-driven editing tasks.The evaluation measures reference alignment and overall preference for these novel tasks.

B.2 Qualitative Results

Qualitative examples show that AnyV2V transfers diverse image edits into videos while preserving relevant scene content, motion, and source-video structure. The framework supports prompt, style, subject, and identity edits through corresponding image-editing models.

  • Prompt-based Editing: AnyV2V performs prompt-based edits such as adding a party hat or changing an airplane’s color while preserving backgrounds and source-video fidelity.InstructPix2Pix supplies the edited first frame for these examples.
  • Reference-based Style Transfer: AnyV2V enables reference-based style transfer from artwork, giving artists direct visual control beyond textual style descriptions.Neural Style Transfer supplies the edited frame from an art reference.
  • Subject-driven Editing: Subject-driven editing replaces objects with reference subjects while maintaining motion, background alignment, and object rotation.Examples replace a cat with a dog and a car with a desired reference car.
  • Identity Manipulation: Identity manipulation swaps a person’s identity by combining InstantID with ControlNet before AnyV2V generates the edited video.The authors note that this particular image-editing combination can alter the background.

B.3.1 Dataset

The human-evaluation dataset combines examples created with several image-editing tools and covers prompt-based and reference-driven video-editing tasks. Evaluators compare baseline and AnyV2V videos for alignment and overall preference.

  • Dataset: The dataset contains 89 samples collected from Pexels for human evaluation.The examples span prompt-based editing, subject-driven editing, Neural Style Transfer, and identity-related tasks.
  • Dataset: Prompt-based examples cover object swapping, object addition, and object removal, while subject-driven examples replace objects with reference subjects.InstructPix2Pix and AnyDoor are used to compose these examples.
  • Dataset: Table 4 reports the number of entries for the Video Editing Evaluation Dataset.
  • Evaluation Protocol: Evaluators select videos that best align with a prompt or reference image and then report their overall preference.The interface presents generated videos from baseline and AnyV2V models.

C.1 Limitations

The paper identifies limitations from imperfect image-editing models and restricted I2V motion capabilities, while also noting societal risks from realistic manipulation. These constraints affect editing reliability, motion fidelity, and responsible deployment.

  • Inaccurate Edit from Image Editing Models: Image-editing models may produce inaccurate initial frames, requiring several attempts and manual selection, especially for subject-driven editing.The authors expect improved image-editing models to reduce this effort.
  • Limited ability of I2V models: Current I2V models may fail to follow fast or complex source-video motion, which the authors associate with training primarily on slow-motion videos.The paper anticipates that more robust I2V models can address this issue.
  • Assets and Licensing: The paper releases code and assets under open licenses, including a human-evaluation dataset and demo videos.
  • Societal Impacts: Realistic object manipulation creates risks of misinformation and privacy violations through counterfeit videos and unauthorized use of a person’s likeness.The paper proposes safeguards such as unseen watermarking and responsible AI frameworks.
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