1. Bibliographic Information

1.1. Title

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

1.2. Authors

The authors are Max Ku, Cong Wei, Weiming Ren, Harry Yang, and Wenhu Chen. Their affiliations include the University of Waterloo, Vector Institute, and Harmony.AI.

1.3. Journal/Conference

The paper is published as a preprint on arXiv (arXiv:2403.14468v4). It has also been reviewed on OpenReview (https://openreview.net/forum?id=RFrJCkw2oa), indicating it has undergone a peer-review process, though not formally published in a journal or conference proceedings at the time of this version. arXiv is a widely recognized platform for disseminating research in fields like AI, often preceding formal publication.

1.4. Publication Year

The paper was published on 2024-03-21T15:15:00.000Z.

1.5. Abstract

This paper introduces AnyV2V, a novel, tuning-free framework for video-to-video editing, addressing limitations in current generative models which often lack desired quality, control, or flexibility. Existing methods either rely on zero-shot adaptation from image models (leading to flickering issues) or require extensive fine-tuning (hindering fluid edits). Many also depend solely on textual input, which can be ambiguous and restrict edit types. AnyV2V simplifies video editing into two core steps: (1) modifying the first frame using an off-the-shelf image editing model, and (2) generating the edited video using an existing image-to-video (I2V) generation model by injecting temporal features. This design allows AnyV2V to leverage diverse image editing tools for a wide array of tasks, including prompt-based editing, reference-based style transfer, subject-driven editing, and identity manipulation, which were previously challenging. It also supports any video length. Evaluations show AnyV2V achieves competitive CLIP-scores for text alignment and temporal consistency but significantly outperforms baselines in human evaluations, demonstrating superior visual consistency and high-quality edits across tasks.

1.6. Original Source Link

Official source: https://arxiv.org/abs/2403.14468v4 PDF link: https://arxiv.org/pdf/2403.14468v4.pdf Publication status: Preprint on arXiv, reviewed on OpenReview.

2. Executive Summary

2.1. Background & Motivation

The field of digital content creation using generative models has seen rapid advancements, particularly in image generation. However, video generation and editing still lag behind, often failing to offer the desired level of quality and user control.

The core problem the paper aims to solve stems from several limitations in existing video editing models:

• Quality and Control Deficiencies: Current state-of-the-art video editing models do not provide the granular control or high-quality output that users seek for fluid video edits.

• Methodological Constraints:

  • Zero-shot approaches: Many methods adapt pre-trained text-to-image (T2I) models in a zero-shot manner. While convenient, these often suffer from flickering issues due to a lack of inherent temporal understanding.
  • Extensive Fine-tuning: Other methods necessitate extensive fine-tuning of models, which is computationally expensive, time-consuming, and can hinder rapid production of video edits.

• Limited Input Modality and Ambiguity: Most existing methods primarily rely on textual input for editing guidance. This reliance can lead to ambiguities in interpretation and limits the complexity and types of edits that can be performed, especially for visual concepts that are hard to describe textually (e.g., specific artistic styles, detailed subject appearances).

• Task Specificity: Previous models are typically designed for specific types of edits, making them non-versatile for a broad range of user demands.

  The paper's entry point or innovative idea is to simplify the complex problem of video editing by disentangling it into two more manageable and well-understood sub-problems: (1) single image editing and (2) image-to-video (I2V) generation with video reference. By doing so, AnyV2V proposes a tuning-free paradigm that can leverage the rapid advancements and rich capabilities of off-the-shelf image editing models.

2.2. Main Contributions / Findings

The AnyV2V framework introduces a novel, tuning-free paradigm for video editing, yielding several significant contributions:

1. A Fundamentally Different Solution: AnyV2V redefines video editing as a simpler image editing problem, effectively breaking down the complex task into two distinct and manageable stages: first-frame image modification and image-to-video (I2V) generation with temporal feature injection. This tuning-free approach eliminates the need for extensive model retraining or fine-tuning per editing task.

2. Extended Applicability to Diverse Editing Tasks: Unlike previous methods often limited to prompt-based editing, AnyV2V can leverage any existing image editing tool. This enables it to support a broad array of video editing tasks, including prompt-based editing, reference-based style transfer, subject-driven editing, and identity manipulation, many of which were previously unattainable or difficult to achieve with high precision.

3. Support for Arbitrary Video Lengths: The framework demonstrates the ability to edit videos beyond the typical training frame lengths of I2V models by intelligently using longer inverted latents. This significantly expands the practical utility of video editing tools for real-world applications.

4. Superior Performance and Consistency: Through comprehensive human evaluations, AnyV2V significantly outperformed baseline methods in terms of visual consistency with the source video and overall quality of edits across all tasks. Quantitatively, it achieved competitive CLIP-scores for text alignment and temporal consistency, while qualitative results showcased its robustness and precision in preserving backgrounds and maintaining motion. For example, in prompt-based editing, AnyV2V was preferred in 46.2% of samples, significantly higher than the best baseline at 20.7%.

   These findings collectively solve the problems of limited control, task specificity, computational overhead, and flickering often associated with prior video editing approaches, offering a highly customizable and efficient solution for content creators.

3. Prerequisite Knowledge & Related Work

3.1. Foundational Concepts

To understand AnyV2V, several foundational concepts in generative AI and video processing are essential:

• Generative Models: These are a class of artificial intelligence models designed to generate new data instances that resemble the training data. In the context of images and videos, they learn the underlying patterns and distributions of data to create novel content.
• Deep Generative Models: A sub-category of generative models that use deep neural networks. Examples include Generative Adversarial Networks (GANs) and Diffusion Models.
• Diffusion Models: These models work by progressively adding Gaussian noise to data and then learning to reverse this process, effectively "denoising" random noise back into coherent data. They have shown remarkable success in generating high-quality images and are the backbone of many text-to-image (T2I) and image-to-video (I2V) models. The paper specifically mentions latent diffusion-based models.
  • Latent Diffusion Models (LDMs) (Rombach et al., 2022): Instead of operating directly on high-resolution pixel space, LDMs perform the diffusion process in a compressed latent space. This makes the process computationally much more efficient while retaining high-quality generation.
• Text-to-Image (T2I) Models: Generative models that create images from textual descriptions (prompts). Stable Diffusion and DALL-E are prominent examples.
• Image-to-Video (I2V) Generation Models: These models take an initial image (often the first frame) and a text prompt, then generate a video that extends from that image while adhering to the prompt and maintaining temporal coherence. AnyV2V leverages existing off-the-shelf I2V models.
• DDIM (Denoising Diffusion Implicit Models) (Song et al., 2020): An efficient sampling algorithm for diffusion models. It allows for faster generation compared to standard Denoising Diffusion Probabilistic Models (DDPMs) and has a deterministic reverse process.
• DDIM Inversion (Mokady et al., 2023; Dhariwal & Nichol, 2021): This is the reverse process of DDIM sampling. Given a generated image or video, DDIM inversion can trace back deterministically to the original latent noise that produced it. This is crucial for editing tasks, as it allows modifying an existing image/video by manipulating its corresponding noise latent and then denoising it again.
• Attention Mechanism: A core component in many modern neural networks, especially Transformers. It allows the model to weigh the importance of different parts of the input data when processing another part.
  • Self-Attention: A type of attention where the model attends to different positions of a single sequence to compute a representation of the same sequence.
  • Spatial Self-Attention: In vision models, this refers to attention within a single image frame, allowing the model to understand relationships between different spatial locations.
  • Temporal Self-Attention: Unique to video models, this allows the model to attend to the same spatial position across different frames, capturing motion and temporal dependencies. The paper defines the attention mechanism as: $$\begin{array} { r } { Q = W ^ { Q } z , K = W ^ { K } z , V = W ^ { V } z , } \\ { \mathrm { A t t e n t i o n } ( Q , K , V ) = \mathrm { S o f t m a x } ( \frac { Q K ^ { \top } } { \sqrt { d } } ) V , } \end{array}$$ where:
    • $Q$ represents the query vectors.
    • $K$ represents the key vectors.
    • $V$ represents the value vectors.
    • $z$ is the input hidden state to the self-attention layer.
    • $W^Q$, $W^K$, $W^V$ are learnable projection matrices that transform $z$ into Q, K, V respectively.
    • $\mathrm{Softmax}(\cdot)$ is the softmax function, normalizing the scores.
    • $Q K^\top$ computes the dot product of queries and keys, indicating similarity.
    • $\sqrt{d}$ is a scaling factor (where $d$ is the dimension of the key vectors) to prevent large dot products from pushing the softmax function into regions with very small gradients.
    • The output of the Attention function is a weighted sum of the value vectors.
• Plug-and-Play (PnP) Diffusion Features (Tumanyan et al., 2023a): This technique, originally for image editing, injects intermediate convolution features and attention scores from a source image's generation process into a new generation process. This allows preserving structural and compositional elements from the source while applying new edits (e.g., from a text prompt). AnyV2V extends this idea to I2V models.
• Classifier-Free Guidance (CFG) (Ho & Salimans, 2022): A technique used in diffusion models to improve the alignment of generated samples with conditional inputs (like text prompts). It involves simultaneously running two denoising processes: one with the condition and one without, and then extrapolating between their outputs. This enhances the influence of the conditioning signal without sacrificing sample quality.

3.2. Previous Works

Previous video editing research can generally be categorized into two main paradigms:

1. Zero-Shot Adaptation from Pre-trained Text-to-Image (T2I) Models:

   • These methods aim to leverage the vast knowledge embedded in large T2I models without extensive video-specific training.
   • Examples: Pix2Video (Ceylan et al., 2023) and Fate-Zero (Qi et al., 2023) used inverted latents from T2I models to retain structural and motion information, propagating edits across frames. TokenFlow (Geyer et al., 2023) employed nearest neighbor fields and inverted latents for temporal consistency. FLATTEN (Cong et al., 2023) used optical flow-guided attention.
   • Limitations: These methods often suffer from flickering (inconsistent appearance across frames) due to their inherent lack of explicit temporal understanding. They tend to treat each frame somewhat independently.

2. Fine-Tuned Motion Modules from Pre-trained T2I or Text-to-Video (T2V) Models:

   • These approaches involve adapting T2I or T2V models by fine-tuning specific motion modules or the entire model on video data.
   • Examples: Tune-A-Video (Wu et al., 2023b) and VideoP2P (Liu et al., 2023b) fine-tuned a T2I model to learn continuous motion. Control-A-Video (Chen et al., 2023b) and ControlVideo (Zhang et al., 2023d) integrated ControlNet (Zhang et al., 2023b) for additional spatial guidance. Fairy (Wu et al., 2023a) combined fine-tuning with caching attention maps. VideoSwap (Gu et al., 2023b) required additional parameter tuning and video feature extraction (like tracking) for consistency. CoDeF (Ouyang et al., 2023) allowed one-shot tuning for first-image edit propagation. UniEdit (Bai et al., 2024) leveraged inverted latent and feature map injection with a pre-trained T2V model.
   • Limitations: While these methods generally achieve better temporal consistency than zero-shot methods, they demand significant computational resources and time for fine-tuning, making them less agile for diverse, on-demand editing tasks. They also often struggle with specific, fine-grained control or novel editing modalities beyond text-to-video (e.g., specific reference images).

3.3. Technological Evolution

The evolution of generative models for visual content has largely progressed from image-centric to video-centric applications. Initially, GANs and later Diffusion Models revolutionized image generation and editing, offering unprecedented realism and control. However, extending these capabilities to video proved challenging due to the added dimension of time, which introduces complexities like temporal consistency, motion coherence, and significantly larger data and computational requirements.

Early video generation efforts often involved auto-regressive generation or specialized GAN architectures. The advent of T2I diffusion models inspired researchers to adapt them for video, leading to the zero-shot and fine-tuning paradigms mentioned above. The focus shifted from merely generating frames to ensuring these frames told a coherent story, maintaining object identity and motion. Recent advancements in image-to-video (I2V) models, which take an image as a strong condition, represent a crucial step towards more controllable video generation.

AnyV2V fits into this timeline by building upon the strengths of these specialized I2V models and the maturity of image editing tools. It acknowledges the difficulty of developing a single, universal video editing model and instead proposes a modular framework that integrates existing, highly capable components.

3.4. Differentiation Analysis

Compared to the main methods in related work, AnyV2V introduces several core differences and innovations:

• Tuning-Free Paradigm: This is AnyV2V's most significant differentiator. Unlike fine-tuning methods (e.g., Tune-A-Video, VideoSwap, CoDeF), AnyV2V requires no parameter tuning per editing task. This drastically reduces computational overhead and time, making it highly efficient and scalable for varied user needs.
• Modality-Agnostic Image Editing Integration: AnyV2V disentangles editing into a first-frame image edit, which can be performed by any off-the-shelf image editing model or even human artistic input. This is a crucial innovation. Previous methods were often constrained by their text encoder's capabilities or specific cross-attention mechanisms (VideoP2P). AnyV2V's flexibility allows it to support:
  • Prompt-based editing (like InstructPix2Pix).
  • Reference-based style transfer (using NST or InstantStyle).
  • Subject-driven editing (using AnyDoor).
  • Identity manipulation (using InstantID). This broad applicability to diverse tasks is a key advantage, as illustrated in Table 1, where AnyV2V is shown to support all four listed editing types, unlike most baselines.

The following are the results from Table 1 of the original paper:

• Enhanced Temporal Consistency and Long Video Support: By using DDIM inversion and careful spatial and temporal feature injection, AnyV2V maintains strong temporal consistency and motion coherence. Crucially, it demonstrates the ability to edit videos longer than the typical training frames of I2V models, which is a notable advancement over methods that are often limited to short video clips.

• Separation of Concerns: AnyV2V effectively separates the "what to edit" (handled by image editing models) from the "how to propagate the edit consistently" (handled by I2V models and feature injection). This modularity allows the framework to benefit directly from advancements in both image editing and I2V generation research.

• Agnostic Backbone: The framework is designed to work with Any I2V Models, offering flexibility in choosing the best I2V backbone for specific needs, as demonstrated by experiments with I2VGen-XL, ConsistI2V, and SEINE.

  In essence, AnyV2V addresses the limitations of previous methods by offering a tuning-free, highly flexible, and comprehensive solution for a wide range of video editing tasks, achieving precise control and superior consistency without the typical computational burden.

4. Methodology

4.1. Principles

The core idea behind AnyV2V is to simplify the complex task of video editing by decomposing it into two distinct and more manageable steps. The fundamental principle is that by leveraging the advanced capabilities of existing off-the-shelf image editing models for the initial frame and the image-to-video (I2V) generation models for temporal propagation, a tuning-free and highly versatile video editing framework can be achieved.

The intuition is that detailed and precise edits are often easier to achieve on a single image. Once the first frame of a video is accurately edited, the challenge shifts to propagating this edit consistently throughout the rest of the video while preserving the original motion and structure. AnyV2V addresses this by conditioning an I2V model on the edited first frame and guiding its generation process with structural and motion information extracted from the source video. This disentangled approach allows for flexible control over the edit content (via the image editor) and robust temporal coherence (via the I2V model and feature injection).

4.2. Core Methodology In-depth (Layer by Layer)

AnyV2V operates as a two-stage pipeline. Given a source video $V^S = \{I_1, I_2, I_3, ..., I_n\}$, where $I_i$ is the $i$-th frame and $n$ is the video length, the process unfolds as follows:

The overall illustration of the AnyV2V video editing pipeline is shown in Figure 2.

该图像是示意图，展示了AnyV2V视频编辑框架的工作流程。首先，输入为源视频 $V^{S}$ 的第一帧 $I_{1}$，通过黑箱图像编辑模型进行编辑，生成编辑后的第一帧 $I^{*}_{1}$。接着进行DDIM反演，得到初始噪声 $z^{S}_{T}$，然后通过I2V模型提取特征，包括CNN特征、空间注意力和时间注意力。最后，通过注入特征生成编辑后的视频，并进行去噪处理，得到最后的编辑视频 $z^{*}_{T}$。

Figure 2: AnyV2V takes a source video $V ^ { S }$ as input. In the first stage, we apply a block-box image editing method on the first frame I _ { 1 } according to the editing task. In the second stage, the source video is inverted to initial noise $z _ { T } ^ { S }$ , which is then denoised using DDIM sampling. During the sampling process, we extract spatial convolution, spatial attention, and temporal attention features from the I2V models'decoder layers. To generate the edited video, we perform a DDIM sampling by fixing $z _ { T } ^ { * }$ as $z _ { T } ^ { T }$ and use the edited first frame as the conditional signal. During sampling, we inject the features and attention into corresponding layers of the model.

4.2.1. Flexible First Frame Editing

In the first stage, the initial frame $I_1$ of the source video is extracted and fed into an off-the-shelf image editing model (denoted as $\phi_{\mathrm{img}}$). This model modifies $I_1$ according to user specifications, which can involve various auxiliary conditions $C$ such as text prompts, masks, reference styles, or subject images. The output of this stage is the edited first frame $I_1^*$.

The flexibility of AnyV2V at this stage is a key strength. It can integrate any advanced image editing method, including:

• Prompt-based image editing (e.g., InstructPix2Pix).

• Image style transfer (e.g., Neural Style Transfer).

• Mask-based image editing or inpainting.

• Identity-preserving image editing (e.g., InstantID).

• Subject-driven image editing (e.g., AnyDoor).

  The ability to use these diverse tools means that the user can perform highly accurate and customizable modifications on the video's starting point, which are then propagated throughout the video.

4.2.2. Structural Guidance using DDIM Inversion

To ensure that the generated edited video $V^*$ maintains the general structure and motion of the source video $V^S$, AnyV2V employs DDIM inversion. This process aims to obtain the latent noise of the source video at each time step $t$.

Formally, given the source video $V^S$, the inverted latent noise $\mathbf{z}_t^S$ for time step $t$ is obtained by reversing the DDIM sampling process. The inversion is performed without a text prompt condition (represented by $\mathcal{O}$ for "empty" or "unconditional") but with the first frame $I_1$ as a condition: $$\begin{array} { r } { \mathbf { z } _ { t } ^ { S } = \mathrm { D D I M \_ I n v } \big ( \epsilon _ { \theta } ( \mathbf { z } _ { t + 1 } , I _ { 1 } , \mathcal { O } , t ) \big ) , } \end{array}$$ where:

• $\mathbf{z}_t^S$ is the inverted latent noise at time step $t$ for the source video.

• $\mathrm{DDIM\_Inv}(\cdot)$ denotes the DDIM inversion operation.

• $\epsilon_\theta(\cdot)$ is the denoising model of the I2V model.

• $\mathbf{z}_{t+1}$ is the latent at the next time step.

• $I_1$ is the first frame of the source video, serving as a conditional input during inversion.

• $\mathcal{O}$ represents unconditional guidance (no text prompt).

• $t$ is the current time step in the diffusion process.

  The ideal scenario is to use the latent noise $\mathbf{z}_T^S$ from the final time step $T$ (which represents the initial pure noise of the source video) as the starting point for generating the edited video. However, the authors note a practical workaround: if certain I2V models produce distorted videos when starting from $\mathbf{z}_T^S$, sampling can be initiated from an earlier time step $T' < T$. This technique helps to stabilize the generation process.

4.2.3. Appearance Guidance via Spatial Feature Injection

While DDIM inverted noise provides structural guidance, it's often insufficient to fully preserve the background and appearance details of the source video, especially after the first frame has been edited. To enforce strong appearance consistency, AnyV2V employs spatial feature injection.

During the denoising process for the edited video, information is "injected" from the source video's denoising path. This involves simultaneously denoising the source video using its DDIM inverted latents $\mathbf{z}_t^S$: $$\mathbf{z}_{t-1}^S = \epsilon_\theta(\mathbf{z}_t^S, I_1, \mathcal{O}, t)$$ From this source denoising path, two types of spatial features are collected:

• Convolution features $\{f^{l_1}\}$: These are extracted from the $l_1$-th residual block in the U-Net decoder (specifically, before the skip connection). These features help guide the background and overall layout.

• Spatial self-attention queries and keys $\{Q_s^{l_2}, K_s^{l_2}\}$: These are collected from specific decoder layers $l_2 = \{l_{low}, l_{low+1}, ..., l_{high}\}$. These capture semantic regions within each frame.

  These collected features ($f^{l_1}$, $Q_s^{l_2}$, $K_s^{l_2}$) are then injected (i.e., replaced) into the corresponding layers of the U-Net during the denoising of the edited video. This injection is performed for both the normal denoising branch and the negative prompt branch (used for classifier-free guidance). To control the strength and timing of this injection, two thresholds are used: $\tau_{conv}$ for convolution features and $\tau_{sa}$ for spatial attention. These thresholds determine the initial number of denoising steps during which the injection occurs.

4.2.4. Motion Guidance through Temporal Feature Injection

Even with spatial feature injection, I2V models might sometimes generate incorrect motion. The authors observe that I2V models often freeze spatial layer parameters (from T2I pre-training) but extensively update temporal layer parameters during training on video data. This suggests that a large portion of motion information is encoded within the temporal layers.

To better reconstruct the source video's motion in the edited video, AnyV2V additionally injects temporal attention features. Similar to spatial attention, temporal self-attention queries $Q_t^{l_3}$ and keys $K_t^{l_3}$ are collected from specific U-Net decoder layers $l_3$ of the source video's denoising process. These are then injected into the corresponding temporal attention layers of the edited video's denoising branches. The temporal attention injection is also controlled by a threshold $\tau_{ta}$, which specifies the number of initial denoising steps during which the injection occurs.

4.2.5. Putting it Together

By combining spatial feature injection (for appearance and structure) and temporal feature injection (for motion) with DDIM inverted noise (for overall structural guidance), the final denoising step for generating the edited video $\mathbf{z}_{t-1}^*$ can be represented as: $$\begin{array} { r } { { \bf z } _ { t - 1 } ^ { * } = \epsilon _ { \theta } ( { \bf z } _ { t } ^ { * } , I _ 1 ^ { * } , { \bf s } ^ { * } , t ; \{ f ^ { l _ { 1 } } , Q _ { s } ^ { l _ { 2 } } , K _ { s } ^ { l _ { 2 } } , Q _ { t } ^ { l _ { 3 } } , K _ { t } ^ { l _ { 3 } } \} ) , } \end{array}$$ where:

• $\mathbf{z}_{t-1}^*$ is the less noisy latent for the edited video at the current time step.

• $\epsilon_\theta(\cdot)$ is the denoising model of the I2V model.

• $\mathbf{z}_t^*$ is the noisy latent for the edited video at the current time step.

• $I_1^*$ is the edited first frame, serving as a strong conditional input.

• $\mathbf{s}^*$ is the target prompt for the edited video (if applicable).

• $t$ is the current time step.

• $\{f^{l_1}, Q_s^{l_2}, K_s^{l_2}, Q_t^{l_3}, K_t^{l_3}\}$ are the injected features collected from the source video's denoising path:

  • $f^{l_1}$ are convolution features from decoder layer $l_1$.
  • $Q_s^{l_2}, K_s^{l_2}$ are spatial attention queries and keys from decoder layers $l_2$.
  • $Q_t^{l_3}, K_t^{l_3}$ are temporal attention queries and keys from decoder layers $l_3$.

• $\epsilon_\theta(\cdot ; \{\cdot\})$ specifically denotes the operation where the intermediate features and attention scores within the U-Net are replaced by the injected features.

  This comprehensive feature injection scheme allows AnyV2V to adapt I2V generation models for video editing in a tuning-free manner, ensuring consistency in appearance, structure, and motion with the source video while incorporating the desired edit from the first frame.

5. Experimental Setup

5.1. Datasets

The experiments primarily focused on evaluating the AnyV2V framework across various video editing tasks. For the human evaluation, a custom dataset was curated:

• Source: The dataset was collected from pexels.com, ensuring all data is governed by the $pexels.com/license$ terms.
• Scale: The total dataset comprised 89 samples.
• Characteristics & Domain: The dataset was manually curated to include a diverse range of images and videos to ensure a balanced representation of objects and scenarios from various domains. Images considered safe were exclusively collected.
• Task-Specific Data Generation: For different editing tasks, specific image editing models were used to generate the initial edited frames, which then served as inputs for the AnyV2V framework.

  • Prompt-based Editing: 45 entries, using InstructPix2Pix (Brooks et al., 2023). Examples include swapping objects, adding objects, and removing objects.

  • Reference-based Style Transfer: 20 entries, using Neural Style Transfer (NST) (Gatys et al., 2015).

  • Identity Manipulation: 13 entries, using InstantID (Wang et al., 2024b) with ControlNet (Zhang et al., 2023b).

  • Subject-driven Editing: 11 entries, using AnyDoor (Chen et al., 2023c). This involves replacing objects with reference subjects.

    The following are the results from Table 4 of the original paper:

    Category	Number of Entries	Image Editing Models Used
    Prompt-based Editing	45	InstructPix2Pix
    Reference-based Style Transfer	20	NST
    Identity Manipulation	13	InstantID
    Subject-driven Editing	11	AnyDoor
    Total	89

The choice of these datasets and image editing models aimed to thoroughly validate AnyV2V's versatility across a wide spectrum of editing modalities and its ability to integrate with diverse image editing tools.

5.2. Evaluation Metrics

The paper employed a combination of human evaluations and automatic metrics to assess the performance of AnyV2V.

5.2.1. Human Evaluation

Human evaluators were tasked with judging the edited videos based on two criteria:

• Alignment: How well the edited video aligns with the provided prompt (for prompt-based editing) or the reference image (for reference-based style transfer, subject-driven editing, and identity manipulation).

• Overall Preference: A subjective measure of the overall quality, naturalness, and desirability of the edited video.

  The interface for human evaluation presented multiple generated videos (from baselines and AnyV2V) to evaluators, who then selected the best options for alignment and overall preference.

5.2.2. Automatic Evaluation Metrics

Following common practices in video generation/editing research (Ceylan et al., 2023; Bai et al., 2024), CLIP-scores were used to quantify text alignment and temporal consistency. CLIP (Radford et al., 2021) stands for Contrastive Language-Image Pre-training, a model trained to understand images and text in a shared embedding space.

• CLIP-Text (Text Alignment)

  • Conceptual Definition: This metric quantifies how well the visual content of the generated video aligns semantically with the given text prompt. A higher score indicates that the video accurately reflects the textual description.
  • Mathematical Formula: The CLIP-Text score is calculated as the average cosine similarity between the text embedding of the editing prompt and the image embedding of each frame in the generated video. Let $P$ be the editing prompt, $T_{CLIP}(P)$ be its CLIP text embedding. Let $V^* = \{I_1^*, I_2^*, ..., I_n^*\}$ be the edited video with $n$ frames. Let $I_{CLIP}(I_i^*)$ be the CLIP image embedding of the $i$-th frame. The cosine similarity between two vectors $A$ and $B$ is defined as: $\mathrm{cos\_sim}(A, B) = \frac{A \cdot B}{\|A\| \|B\|}$. $$\mathrm{CLIP\_Text} = \frac{1}{n} \sum_{i=1}^{n} \mathrm{cos\_sim}(T_{CLIP}(P), I_{CLIP}(I_i^*))$$
  • Symbol Explanation:
    • $n$: Total number of frames in the edited video.
    • $P$: The text prompt used for editing.
    • $T_{CLIP}(P)$: The CLIP embedding for the text prompt $P$.
    • $I_i^*$: The $i$-th frame of the edited video.
    • $I_{CLIP}(I_i^*)$: The CLIP embedding for the $i$-th frame.
    • $\mathrm{cos\_sim}(\cdot, \cdot)$: The cosine similarity function, which measures the cosine of the angle between two non-zero vectors. Its value ranges from -1 (completely dissimilar) to 1 (perfectly similar).

• CLIP-Image (Temporal Consistency)

  • Conceptual Definition: This metric assesses the visual coherence and smoothness between consecutive frames in a video. A high score implies that the appearance and content transitions between frames are smooth and consistent, without abrupt changes or flickering.
  • Mathematical Formula: The CLIP-Image score is calculated as the average cosine similarity between the CLIP image embeddings of every pair of consecutive frames in the generated video. $$\mathrm{CLIP\_Image} = \frac{1}{n-1} \sum_{i=1}^{n-1} \mathrm{cos\_sim}(I_{CLIP}(I_i^*), I_{CLIP}(I_{i+1}^*))$$
  • Symbol Explanation:
    • $n$: Total number of frames in the edited video.
    • $I_i^*$: The $i$-th frame of the edited video.
    • $I_{i+1}^*$: The $(i+1)$-th frame of the edited video (the frame immediately following $I_i^*$).
    • $I_{CLIP}(I_i^*)$: The CLIP embedding for the $i$-th frame.
    • $I_{CLIP}(I_{i+1}^*)$: The CLIP embedding for the $(i+1)$-th frame.
    • $\mathrm{cos\_sim}(\cdot, \cdot)$: The cosine similarity function.

5.3. Baselines

For prompt-based editing tasks, AnyV2V was compared against three established baseline models:

• Tune-A-Video (Wu et al., 2023b): A method that fine-tunes an image diffusion model for text-to-video generation.

• TokenFlow (Geyer et al., 2023): A zero-shot method that uses consistent diffusion features for consistent video editing.

• FLATTEN (Cong et al., 2023): A method leveraging optical flow-guided attention for consistent text-to-video editing.

  For the novel tasks (reference-based style transfer, subject-driven editing, and identity manipulation), the paper notes that no publicly available baseline methods exist. Therefore, the evaluation focused on comparing the performance of different I2V generation models (I2VGen-XL, ConsistI2V, SEINE) when integrated within the AnyV2V framework.

5.4. Implementation Details

• I2V Generation Models: AnyV2V was implemented and tested with three off-the-shelf I2V models:
  • I2VGen-XL (Zhang et al., 2023c)
  • ConsistI2V (Ren et al., 2024)
  • SEINE (Chen et al., 2023d)
• Injection Thresholds:
  • \tau_{conv} = 0.2T: Threshold for convolution feature injection.
  • \tau_{sa} = 0.2T: Threshold for spatial attention injection.
  • \tau_{ta} = 0.5T: Threshold for temporal attention injection.
  • $T$: Total number of sampling steps (default values of the selected I2V models).
• DDIM Sampler: The DDIM (Song et al., 2020) sampler was used.
• U-Net Layers for Injection:
  • Convolution feature injection: $l_1 = 4$.
  • Spatial and temporal attention injections: $l_2 = l_3 = \{4, 5, 6, ..., 11\}$. These layers were chosen to capture both semantic and motion information effectively.
• Classifier-Free Guidance (CFG): Applied to all models with a negative prompt to improve generation quality: "Distorted, discontinuous, Ugly, blurry, low resolution, motionless, static figureismb, Ugly ac ncpltears" (a common negative prompt for diffusion models).
• Image Editing Model Candidates:
  • InstructPix2Pix (Brooks et al., 2023) for prompt-based image editing.
  • Neural Style Transfer (NST) (Gatys et al., 2015) for style transfer.
  • AnyDoor (Chen et al., 2023c) for subject-driven image editing.
  • InstantID (Wang et al., 2024b) for identity-driven image editing.
• Computational Resources: All experiments were conducted on a single Nvidia A6000 GPU.
• Inference Time: Editing a 16-frame video required approximately 15GB of GPU memory and about 100 seconds for the entire inference process.

6. Results & Analysis

6.1. Core Results Analysis

AnyV2V demonstrates strong performance across a variety of video editing tasks, both qualitatively and quantitatively. Its core strength lies in its ability to deliver precise, high-quality, and temporally consistent edits across diverse scenarios, leveraging the power of off-the-shelf image editing models and I2V generation models.

6.1.1. Prompt-based Editing

Qualitatively, AnyV2V excels in prompt-based editing by accurately modifying target areas while preserving the background and fidelity of the original video. For instance, it can precisely place a party hat on a man's head or change an airplane's color to blue (Figure 3). Compared to baselines, AnyV2V avoids unwarranted changes and flickering effects, especially for motion tasks like adding snowing weather, where I2V models inherently support animating dynamic scenes.

该图像是一个示意图，展示了AnyV2V模型在多种基于提示的编辑任务中的表现。第一行是源视频，第二到四行展示了不同编辑方法的结果，最后一行为AnyV2V处理的结果，效果更为流畅和一致。

Figure 3: AnyV2V is robust in a wide range of prompt-based editing tasks while preserving the background The results align the most with the text prompt and maintain high motion consistency.

Quantitatively, human evaluations reveal a significant preference for AnyV2V.

The following are the results from Table 2 of the original paper:

As seen in Table 2, AnyV2V (I2VGen-XL) achieves the highest scores in both human evaluation categories (Alignment: 69.7%, Overall: 46.2%), significantly outperforming FLATTEN (best baseline at 31.7% and 20.7% respectively). In terms of CLIP Scores, AnyV2V (I2VGen-XL) also achieves the highest CLIP-Text (0.2932), indicating strong text alignment, and competitive CLIP-Image (0.9652), suggesting good temporal consistency. The authors attribute this success to AnyV2V's compatibility with state-of-the-art image editing models.

6.1.2. Reference-based Style Transfer, Subject-Driven Editing, and Identity Manipulation

For these novel tasks, where AnyV2V pioneers the application of tuning-free video editing, qualitative results (Figure 4) are compelling.

• Style Transfer: AnyV2V can accurately capture and propagate specific artistic styles from reference images (e.g., Kandinsky, Van Gogh) throughout a video, which is difficult with text-guided methods.

• Subject-Driven Editing: It effectively replaces subjects (e.g., a cat with a dog, one car with another) while maintaining highly aligned motion, background, and specific details like a spinning wheel.

• Identity Manipulation: The framework can swap a person's identity based on a reference image, providing unprecedented flexibility in video personalization.

  该图像是图示，展示了AnyV2V在不同编辑任务上的效果，包括基于参考的风格转移、主题驱动的编辑，以及身份操控。通过源视频和参考图像，AnyV2V实现了高质量的视频编辑。各个任务的效果均在图中展示，表明该方法的灵活性和多样性。

Figure 4: With different image editing models, AnyV2V can achieve a wide range of editing tasks, includin reference-based style transfer, subject-driven editing, and identity manipulation.

Quantitative human evaluations for these novel tasks (Table 5 in Appendix B) further confirm the robustness of AnyV2V, with AnyV2V (I2VGen-XL) consistently being the most preferred backbone.

The following are the results from Table 5 of the original paper:

Table 5 clearly shows that AnyV2V (I2VGen-XL) achieves the highest reference alignment and overall preference across all three novel tasks, especially dominating Reference-based Style Transfer (100% alignment, 76.9% overall) and Subject-driven Editing (93.9% alignment, 84.8% overall). This underscores its robustness and versatility. The Image Editing Success Rate highlights that the quality of the initial first-frame edit is crucial, especially for challenging tasks like Subject-driven Editing with AnyDoor (only $\approx 10\%$ success rate for the initial image edit).

6.1.3. I2V Backbones Comparison

The choice of I2V backbone significantly impacts the final quality:

• AnyV2V (I2VGen-XL): Most robust, offering good generalization for consistent motions and high visual quality.
• $AnyV2V (ConsistI2V)$: Generates consistent motion but occasionally suffers from watermarks due to training data, affecting visual quality.
• AnyV2V (SEINE): Weaker generalization, but produces consistent and high-quality videos for simpler motions (e.g., a walking person).

6.1.4. Editing Video beyond Training Frames of I2V Model

A notable finding is AnyV2V's capability to edit videos longer than the typical 16-frame limit of I2V models. Instead of auto-regressive generation (which causes semantic inconsistency), AnyV2V leverages longer inverted latents as initial noise. The experiments confirm that these inverted latents contain sufficient temporal and semantic information to allow the I2V model to generate longer videos while maintaining consistency (Figure 5).

该图像是示意图，展示了 AnyV2V 框架在长视频编辑中的效果。第一行是源视频帧，第二行是经过编辑的帧，编辑主题为“将女人变成机器人”。该方法保留了运动一致性，并可处理长视频编辑任务。

Figure 5: AnyV2V can edit video length beyond the training frame while maintaining motion consistency. The first row is the source video frames while the second rows are the edited. The editing prompt of the image was "turn woman into a robot" using image model InstructPix2Pix (Brooks et al., 2023).

6.2. Ablation Studies / Parameter Analysis

The ablation study confirms the criticality of temporal feature injection, spatial feature injection, and DDIM inverted noise for AnyV2V's performance.

The following are the results from Table 3 of the original paper:

该图像是图表，展示了不同编辑方法下，AnyV2V在视频编辑任务中的视觉比较，包含源视频和经过不同处理的结果，从左到右分别为源视频、AnyV2V、AnyV2V-TL、AnyV2V-SI、AnyV2V-SD-I等，展示了各方法在视觉效果方面的差异。

Figure 6: Visual comparisons of AnyV2V's editing results after disabling temporal feature injection (T.I.) spatial feature injection (S.I.) and DDIM inverted initial noise (D.I.).

6.2.1. Effectiveness of Temporal Feature Injection

Disabling temporal feature injection (w/o T. Injection) leads to edited videos with less adherence to the source video's motion, even if the CLIP-Image score (Table 3) shows a slight, counter-intuitive increase (0.9652 vs 0.9648). Qualitatively (Figure 6, couple sitting example, 3rd row, 2nd column), the woman's leg-raising motion is lost without temporal injection, highlighting its importance for capturing subtle motion nuances.

6.2.2. Effectiveness of Spatial Feature Injection

Removing spatial feature injection (w/o T. Injection & S. Injection) results in a drop in CLIP-Image score (0.9637) and significant qualitative degradation. Figure 6 shows incorrect subject appearance and pose (ballet dancing example) and degenerated backgrounds (couple sitting example). This confirms that spatial feature injection is crucial for preserving the source video's structure and appearance, as DDIM inverted noise alone is often insufficient.

6.2.3. DDIM Inverted Noise as Structural Guidance

Replacing DDIM inverted noise with random noise (the most severe ablation, w/o T. Injection & S. Injection & DDIM Inversion) leads to the lowest CLIP-Image score (0.9607) and severely degraded visual appearance (Figure 6). The I2V model struggles to animate the input image when the editing prompt is out-of-domain, underscoring the fundamental importance of DDIM inverted noise for providing structural guidance.

6.2.4. U-Net Layers for Feature Injection

The study explored where to inject features. Visualization of convolution, spatial attention, and temporal attention features across I2V model decoder layers (Figure 7) showed:

• Convolution Features: Earlier layers capture overall layout, deeper layers capture high-frequency details. $l_1=4$ for convolution feature injection was chosen to inject background/layout guidance without excessive high-frequency details.

• Spatial Attention: Represents semantic regions.

• Temporal Attention: Highlights foreground moving subjects (e.g., a running woman).

• Selected Layers: $l_2=l_3=\{4, 5, ..., 11\}$ were chosen for spatial and temporal attention injection to preserve semantic and motion information.

  该图像是图表，展示了I2V生成模型在视频采样中的卷积特征、空间注意力和时间注意力得分的可视化。第一行显示生成的视频，后续行分别为不同层次的特征图和注意力得分，展示不同层次的特征表现。

Figure7:Visualizations of the convolution, spatial attention and temporal attention features during video sampling for I2V generation models' decoder layers. We feed in the DDIM inverted noise to the I2V models such that the generated videos (first row) are reconstructions of the source video.

6.2.5. Ablation Analysis on Feature Injection Thresholds

• Spatial Injection Thresholds ($\tau_{conv}, \tau_{sa}$): Figure 8 illustrates the impact. If disabled, videos fail to adhere to layout/motion. If too high (e.g., T), high-frequency details from the source (e.g., textures) corrupt the edited video. \tau_{conv} = \tau_{sa} = 0.2T was found to be the optimal setting, balancing layout preservation with preventing unwanted detail injection.

  该图像是一个比较不同超参数设置的结果展示，展示了在多个时间帧下（Frame 1、Frame 10 和 Frame 16）对源视频进行编辑的效果。不同的 $au_{c,s}$ 值（包括 0、0.2T、0.5T、0.7T 和 T）分别影响编辑视频的布局与结构。

Figure 8: Hyperparameter study on spatial feature injection. We find that $\\tau _ { s a } = 0 . 2 T$ is the best setting for maintaining the layout and structure in the edited video while not introducing unnecessary visual details from the source video. $\\tau _ { c , s }$ represents $\\tau _ { c o n v }$ and $\\tau _ { s a }$ . (Editing prompt: teddy bear running. The experiment was conducted with the I2VGen-XL backbone.

• Temporal Injection Threshold ($\tau_{ta}$): Figure 9 shows the effect.

  • \tau_{ta} < 0.5T: Motion guidance is too weak, leading to partially aligned motions.

  • \tau_{ta} > 0.5T: Stronger adherence to motion but potential for distortion.

  • \tau_{ta} = 0.5T: Selected as the optimal value, striking a balance among motion alignment, motion consistency, and video fidelity.

    该图像是一个超参数研究图，展示了不同的 $au_{c,s}$ 设置（从 0 到 T）对视频帧处理的影响。每一列代表不同的 $au_{c,s}$ 值，包含帧 1、帧 10 和帧 16 的输出结果，显示出在调整超参数后，生成视频的变化和运动一致性。

Figure 9: Hyperparameter study on temporal feature injection. We find that $\\tau _ { t a } = 0 . 5 T$ to be the optimal setting as it balances motion alignment, motion consistency, and video fidelity. (Editing prompt: darth vader walking. The experiment was conducted with the SEINE backbone.

7. Conclusion & Reflections

7.1. Conclusion Summary

The paper successfully introduced AnyV2V, a pioneering tuning-free framework for a wide range of video-to-video editing tasks. By cleverly decomposing video editing into two core steps – initial frame modification using off-the-shelf image editing models and subsequent video generation via an I2V model guided by temporal feature injection and DDIM inverted latents – AnyV2V overcomes significant limitations of prior methods. It offers unprecedented flexibility, supporting prompt-based editing, reference-based style transfer, subject-driven editing, and identity manipulation, many of which were previously challenging or unattainable. Crucially, AnyV2V can handle videos of any length, extending beyond the typical training limits of I2V models. Extensive experiments, including human evaluations, demonstrated that AnyV2V achieves superior visual consistency and high-quality edits compared to existing state-of-the-art baselines, while maintaining competitive CLIP-scores. The ablation studies meticulously validated the importance of each component, confirming that spatial and temporal feature injection alongside DDIM inverted noise are critical for robust performance.

7.2. Limitations & Future Work

The authors candidly acknowledge several limitations and suggest future research directions:

• Inaccurate Edits from Image Editing Models: AnyV2V's reliance on initial frame editing means its performance is directly tied to the capabilities and reliability of the off-the-shelf image editing models. Current state-of-the-art image editors are not yet perfectly consistent or precise (Ku et al., 2024), sometimes requiring multiple attempts to achieve a satisfactory first frame (e.g., AnyDoor for subject-driven editing). This necessitates manual effort in picking a good edited frame. Future advancements in image editing models are expected to mitigate this limitation.
• Limited Ability of I2V Models for Fast/Complex Motion: The framework's ability to propagate motion accurately is constrained by the underlying I2V models. If the source video contains fast (e.g., billiard balls colliding) or complex (e.g., intricate hair clipping) motions, AnyV2V may struggle to follow them, even with motion guidance. This is attributed to many current I2V models being primarily trained on slow-motion videos. The authors anticipate that more robust I2V models (trained on a wider variety of motions) will resolve this issue.
• Future Work - Bridging I2V to T2V Models: A key future direction proposed is to find a tuning-free method to integrate the strengths and properties of I2V models into text-to-video (T2V) models. This would allow leveraging the capabilities of stronger T2V models for even broader and more advanced video editing applications.

7.3. Personal Insights & Critique

AnyV2V presents a highly elegant and practical solution to the complex problem of video editing. The tuning-free and two-stage decomposition paradigm is a significant conceptual shift, moving away from monolithic, task-specific video models towards a modular framework that intelligently combines specialized components. This approach is highly appealing due to its:

• Modularity and Extensibility: The ability to plug-and-play with any image editing model means AnyV2V can instantly inherit new capabilities and improvements from the rapidly evolving field of image editing. This future-proofs the framework to a large extent.

• User Control: By allowing detailed first-frame edits, it offers a level of precise visual control that text-only prompts often lack, directly addressing a critical user need.

• Efficiency: The tuning-free nature dramatically reduces computational costs and time, making advanced video editing more accessible and practical for everyday content creation.

  However, a potential area for improvement or a nuanced critique lies in its reliance on the first frame. While powerful, editing only the initial frame might be insufficient for scenarios where the desired edit needs to be initiated or change midway through the video. While the paper mentions adapting to any video length, the initial edit is still tied to $I_1$. Future extensions could explore methods for mid-video editing or localized edits not necessarily starting from the first frame, perhaps by adapting the DDIM inversion and feature injection to arbitrary starting points within a video.

The authors' proactive discussion of societal impacts, particularly the risks of misinformation spread and privacy violations due to realistic fabricated videos (similar to DeepFakes), is commendable. Their suggestion of unseen watermarking as a safeguard highlights a responsible approach to developing powerful generative technologies. This paper's methodology, particularly the feature injection strategy, could potentially be transferred to other domains requiring temporal consistency in generative tasks, such as 3D asset generation over time or medical image sequence synthesis, where maintaining structural and motion coherence is paramount. The clear and thorough ablation studies also provide valuable insights into the roles of different attention mechanisms in I2V models, which could inform the design of future video diffusion architectures.