FreeViS: Training-free Video Stylization with Inconsistent References

## Abstract

Abstract Video stylization plays a key role in content creation, but it remains a challenging problem. Naïvely applying image stylization frame-by-frame hurts temporal consistency and reduces style richness. Alternatively, training a dedicated video stylization model typically requires paired video data and is computationally expensive. In this paper, we propose FreeViS, a training-free video stylization framework that generates stylized videos with rich style details and strong temporal coherence. Our method integrates multiple stylized references to a pretrained image-to-video (I2V) model, effectively mitigating the propagation errors observed in prior works, without introducing flickers and stutters. In addition, it leverages high-frequency compensation to constrain the content layout and motion, together with flow-based motion cues to preserve style textures in low-saliency regions. Through extensive evaluations, FreeViS delivers higher stylization fidelity and superior temporal consistency, outperforming recent baselines and achieving strong human preference. Our training-free pipeline offers a practical and economic solution for high-quality, temporally coherent video stylization. The code and videos can be accessed via https://xujiacong.github.io/FreeViS/

## 1 Introduction

Style transfer has been a long-standing research topic in computer vision, with applications spanning art, education, advertising, and entertainment. In recent years, tremendous progress has been made in image style transfer Gatys et al. (2016); Huang & Belongie (2017); Liu et al. (2021); Chung et al. (2024); Xu et al. (2025b) , driven by advances in deep learning architectures and generative modeling. While the focus has been on still images, videos have become a more popular medium for everyday communication in recent years. Yet video stylization remains significantly underexplored compared to its image-based counterpart. As content distribution changes across time, naïvely applying image stylization frame by frame leads to severe flickering artifacts, resulting in poor temporal consistency. Even though advanced temporal smoothing techniques Duan et al. (2023) can mitigate flickering artifacts, they often do so at the expense of losing style richness, resulting in overly smoothed textures and reduced visual plausibility.

Previous video stylization works Wang et al. (2020); Pande & Sharma (2023) have attempted to improve temporal consistency by modifying convolutional architectures. However, their quality remains inferior to that of recent diffusion-based image stylization approaches. More recently, several diffusion-based methods Liu et al. (2024a); Ye et al. (2025) have been proposed for stylized text-to-video (T2V) generation, but these approaches are not directly applicable to video-to-video (V2V) editing. In addition, our experiments also found that neither recent unified video editing frameworks Jiang et al. (2025) nor text-driven editing methods Geyer et al. (2024) are able to perfectly transfer style across videos. Beyond these works, directly fine-tuning a video diffusion model (*e.g*., DiT-based architectures) is also impractical, as it demands large-scale original–stylized video pairs, which are difficult to obtain. Simply adopting the perceptual training strategy, as in image stylization Liu et al. (2021), cannot ensure temporal consistency across frames. Moreover, substantial computational resources are required for fully finetuning.

On the other hand, reference-based video editing methods, such as AnyV2V Ku et al. (2024), can leverage image stylization models to stylize the first frame, and then propagate it to the rest of the frames using pretrained image-to-video (I2V) models. In this way, no training or only limited training Ouyang et al. (2024) is required. However, due to the absence of stylized videos in the training phase of the base I2V model, it fails to properly handle the reference frame as it is out of the training distribution (see Appendix [A.7](https://arxiv.org/html/2510.01686v1#A1.SS7)), thus incapable of parsing and propagating style patterns from the first edited frame. As Figure [1](https://arxiv.org/html/2510.01686v1#S0.F1) shows, these reference-based methods struggle to transfer style patterns to novel content in subsequent frames, resulting in a pronounced propagation error.

In this paper, we aim to tackle video stylization in a training-free manner, *i.e*. by inverting a diffusion model, to achieve comprehensive style transfer while preserving temporal coherence. An intuitive solution to address propagation error is to provide the base model with multiple reference frames across the entire video. However,
naïvely concatenating additional reference frames with noise latent leads to severe flickering and stuttering artifacts in the results. To address this, we introduce isolated attention with a carefully designed masking strategy to mitigate stylization inconsistencies, together with a novel approach to inject temporal dynamics into the reference latents. We also found that the inverted noises alone are insufficient to accurately reconstruct original video content, often missing structures in stylized results. To this end, we propose a strategy to extract high-frequency components from the reconstruction compensation of PnP inversion Ju et al. (2024) to compensate and constrain spatial layouts and motion trajectories.
Lastly, in plain regions with few salient features, style textures may vanish under large camera motion. To overcome this, we propose a method that leverages optical flow to constrain diffused attention areas in associated frames.

The contributions of this paper are summarized as follows: (1) We propose a novel reference-based and training-free framework: FreeViS, for video stylization, which effectively addresses the propagation error inherent in existing methods; (2) We conduct extensive experiments demonstrating the effectiveness of our design and showing that FreeViS achieves higher stylization quality compared to prior approaches; (3) We show that, when combined with existing T2V models, FreeViS also achieves stylized T2V generation with performance comparable to recent training-based methods.

## 2 Observations

### 2.1 Frequency Analysis of Intermediate Latents

Since the inverted noises alone cannot accurately reconstruct the videos (Appendix [A.7](https://arxiv.org/html/2510.01686v1#A1.SS7)),
we adopt PnP Inversion Ju et al. (2024) to progressively align the denoising trajectory with the inversion path. Building on this framework, we investigate the influence of low-frequency (LF) and high-frequency (HF) components of intermediate latents along the denoising process (Appendix [A.8](https://arxiv.org/html/2510.01686v1#A1.SS8)). Recent image editing studies Yang et al. (2023); Xu et al. (2025b) have shown that the HF components of the initial noise primarily determine the spatial layout of the generated images.
Here, we observe a similar phenomenon in I2V models: LF latents primarily govern the appearance and color distribution of the generated video, whereas HF components encode layout and motion cues.

### 2.2 Cross-frame Attention

Recent advances in video generation Kong et al. (2024); Wan et al. (2025) have seen a shift in architectural design from UNet-based models Blattmann et al. (2023) to Transformer-based DiT frameworks Peebles & Xie (2023).
For finer-grained analysis of attention patterns, we decompose the cross-frame attention in the I2V Wan model into temporal and spatial components . To visualize temporal attention, we spatially average the attention values across each frame.
For spatial attention, we select a point on the query frame and record the distribution of attention intensities across other frames. As shown in Figure [2](https://arxiv.org/html/2510.01686v1#S2.F2), the model exhibits an auto-causal attention pattern, with lower attention values in the upper triangular region of the temporal attention matrices. Notably, the second frame (immediately following the reference frame) consistently receives high attention throughout the entire denoising process. This suggests that the reference frame can influence the generation of all subsequent frames indirectly, by strongly guiding the denoising of the second frame.

Figure: Figure 2: Visualization of cross-frame temporal (upper) and spatial (lower) attentions in different timesteps.
Refer to caption: x2.png

During late stages of denoising, the model concentrates on refining local details, such as textures, resulting in more accurate pixel-level correspondences compared to earlier stages that primarily require global information for layout establishment. This is reflected in the sharper and more focused high-attention regions. However, when attending to frames that are temporally distant from the query frame, the pixel-level attention tends to diffuse into incorrect areas, likely due to increased motion (camera or objects) between distant frames relative to adjacent ones. Note that accurate pixel correspondences are crucial for preserving the consistency of local stylized textures or strokes in reference-based video stylization. Therefore, an external control strategy is required to regularize the attention area for better appearance preservation.

## 3 Method

The overall architecture of the proposed FreeViS pipeline is illustrated in Figure [3](https://arxiv.org/html/2510.01686v1#S3.F3). Our framework builds upon a pretrained I2V diffusion model, which serves as the backbone. Given an arbitrary style image, stylized references are generated via an image style transfer model applied to several selected content video frames. We leverage inversion to recover the denoising trajectory and initial noise. The pipeline comprises two branches: reconstruction and stylization. Each branch is conditioned on the corresponding selected references.
The reconstruction branch provides query, key, and value matrices to the stylization branch in every DiT block. Parameters specific to the reconstruction and stylization branches are denoted by superscripts $r$ and
$s$, respectively. The subscript $R$ denotes parameters associated with additional references beyond the first frame. After each denoising step, the high-frequency components of the difference between the target latent $\mathbf{x}_{t}$ and reconstruction latent $\mathbf{x}^{r}_{t}$ are added to the stylization latent $\mathbf{x}^{s}_{t}$. The value matrices $\mathbf{v}^{s}_{R}$ projected from additional stylized references are injected with dynamic clues, as formalized in Equation [4](https://arxiv.org/html/2510.01686v1#S3.E4).

Figure: Figure 3: Overview of the FreeViS pipeline. Isolated-Attn indicates the ① mode, while MIsolated-Attn includes ② and ③ attention modes. Optical flow, extracted using RAFT Teed & Deng (2020), generates reference and flow masks for masked attention in attention modes ② and ③.
Refer to caption: x3.png

### 3.1 Indirect High-frequency Compensation

We adopt PnP Inversion Ju et al. (2024) to provide additional guidance throughout the denoising process. Specifically, the full denoising trajectory $[\mathbf{x}_{t},\mathbf{x}_{t-1},\ldots,\mathbf{x}_{0}]$ is cached during inversion and treated as the target latents. In the original PnP framework, the difference between the target latent $\mathbf{x}_{t}\in\mathbb{R}^{s\times c\times h\times w}$, where $s$ is the number of encoded latent maps, and the current reconstruction latent $\mathbf{x}_{t}^{r}\in\mathbb{R}^{s\times c\times h\times w}$ is computed and applied to both the reconstruction and editing branches, enabling near-exact reconstruction. However, we observe that this strong correction directly drives all the information of the stylized latent toward the original target, resulting in undesirable color distribution shifts from the stylized video back toward the original content.

As analyzed in Section [2.1](https://arxiv.org/html/2510.01686v1#S2.SS1), low-frequency (LF) latents primarily control appearance and color, while high-frequency (HF) components encode layout and motion. This motivates our Indirect High-frequency Compensation (IHC) strategy, which injects only HF differences into the stylized latents, preserving the stylized appearance while correcting structural inconsistencies. In the reconstruction branch, full compensation is applied to recover video content: $\mathbf{x}_{t}^{r}=\lambda\cdot(\mathbf{x}_{t}-\mathbf{x}_{t}^{r})+\mathbf{x}_{t}^{r}$,
where $\lambda$ is a hyperparameter linearly decaying over timesteps. To further align the color distribution, we firstly apply AdaIN Huang & Belongie (2017) on both $\mathbf{x}_{t}$ and $\mathbf{x}_{t}^{r}$ before calculating their difference:

$$ $\mathcal{T}(\mathbf{x}_{t})=\sigma(\mathbf{x}_{t}^{s})\left(\frac{\mathbf{x}_{t}-\mu(\mathbf{x}_{t})}{\sigma(\mathbf{x}_{t})}\right)+\mu(\mathbf{x}_{t}^{s}),\qquad\mathcal{T}(\mathbf{x}_{t}^{r})=\sigma(\mathbf{x}_{t}^{s})\left(\frac{\mathbf{x}_{t}^{r}-\mu(\mathbf{x}_{t}^{r})}{\sigma(\mathbf{x}_{t}^{r})}\right)+\mu(\mathbf{x}_{t}^{s}),$ (1) $$

where $\mathbf{x}_{t}^{s}$ refers to the stylization latents in current timestep $t$, and $\mu(\cdot)$ and $\sigma(\cdot)$ denote channel-wise mean and standard
deviation, respectively.
We then compute the Fast Fourier Transform (FFT) of the reconstruction difference along spatial dimensions to obtain the frequency representation. A low-pass filter $H_{LP}$ is applied to isolate HF components, which are subsequently transformed back via inverse FFT (iFFT) and added to the stylized latent:

$$ $\mathbf{x}_{t}^{s}=\lambda\cdot\mathcal{F}^{-1}\left(\mathcal{F}\left(\mathcal{T}(\mathbf{x}_{t})-\mathcal{T}(\mathbf{x}_{t}^{r})\right)\cdot(1-H_{LP})\right)+\mathbf{x}_{t}^{s},$ (2) $$

where $\mathcal{F}(\cdot)$ and $\mathcal{F}^{-1}(\cdot)$ denote FFT and iFFT. Empirically, IHC successfully preserves the stylized color and texture while enhancing spatial
consistency and motion fidelity, particularly in scenes with significant camera movement or when later frames differ substantially from the first frame.

### 3.2 Additional Inconsistent References

Previous I2V-based video editing methods Ku et al. (2024); Ouyang et al. (2024) typically rely on a single stylized first frame to guide the stylization of the entire video. While effective when content changes are minimal, this approach struggles to propagate style features to regions that differ significantly from the first frame. An intuitive solution is to incorporate multiple stylized references. However, existing advanced I2V models support only a single reference input.
Naively concatenating additional reference tokens with reconstruction or stylization noise tokens prior to self-attention often results in significant flickering and stuttering in the generated videos.

To enable multi-reference inputs in the reconstruction branch, we propose the Isolated-Attn strategy,
which isolates the influence of auxiliary references $\mathbf{x}^{r}_{R}$, thereby preventing interference with the original denoising schedule. Specifically, the reconstruction tokens $\mathbf{x}^{r}$ undergo standard self-attention, while the reference tokens $\mathbf{x}^{r}_{R}$ attend to both reconstruction and reference keys and values. This design allows reference tokens to evolve in sync with the denoising process, mimicking full self-attention behavior. Denote the Query, Key, and Value matrices projected from $\mathbf{x}^{r}$ and $\mathbf{x}^{r}_{R}$ as $\{\mathbf{Q}^{r},\mathbf{K}^{r},\mathbf{V}^{r}\}$ and $\{\mathbf{Q}^{r}_{R},\mathbf{K}^{r}_{R},\mathbf{V}^{r}_{R}\}$, respectively. Then, this mechanism can be defined as:

$$ $\mathbf{Out}^{r}=\mathcal{A}(\mathbf{Q}^{r},\mathbf{K}^{r},\mathbf{V}^{r})\oplus\mathcal{A}(\mathbf{Q}_{R}^{r},\mathbf{K}^{r}\oplus\mathbf{K}_{R}^{r},\mathbf{V}^{r}\oplus\mathbf{V}_{R}^{r}),$ (3) $$

where $\mathcal{A}(\cdot)$ refers to attention operation, and $\oplus$ denotes sequence-wise token concatenation.

Differently, in the stylization branch, full information exchange among all tokens is essential for comprehensive style transfer. However, since the additional stylized references $\mathbf{x}^{s}_{R}$ are independently encoded, the value matrices $\mathbf{V}^{s}_{R}$ projected from $\mathbf{x}^{s}_{R}$ lack dynamic information, which causes the stuttering issue in adjacent frames. We observe that appearance information can be isolated and exchanged between videos with shared dynamics (see details in Appendix [A.5](https://arxiv.org/html/2510.01686v1#A1.SS5)).
Since the dynamic information is shared between stylization values $\mathbf{V}^{s}$ and reconstruction values $\mathbf{V}^{r}$, we decouple the dynamic residual by computing their differences relative to their respective references (only contain appearance), and inject only the dynamic component into $\mathbf{V}^{s}_{R}$. This is implemented as:

$$ $\mathbf{V}_{R}^{s}=\mathbf{V}_{R}^{s}+\xi\cdot\left(\mathbf{V}^{s}[i_{R}]-\mathbf{V}_{R}^{s}\right)+(1-\xi)\cdot\left(\mathbf{V}^{r}[i_{R}]-\mathbf{V}_{R}^{r}\right),$ (4) $$

where $i_{R}$ denotes the positional indices of the references, and $\xi$ increases linearly from 0 to 1 over the timesteps. Early in the denoising process, the model relies more on dynamics from the reconstruction branch; toward the end, $\mathbf{V}_{R}^{s}$ converges to $\mathbf{V}^{s}[i_{R}]$ to ensure consistent final outputs.

Since inconsistencies naturally exist across stylized references, the same region may exhibit varying appearances, resulting in time-variant stylization artifacts, such as flickering. To address this issue, we leverage optical flow extracted from the content video to identify regions already covered by earlier references and mask these regions in attention, thereby resolving appearance conflicts. We employ RAFT Teed & Deng (2020) to compute optical flow and trace pixel correspondences from the first reference frame to subsequent reference frames. The reference mask $M_{Ref}$ is constructed such that a position is marked as False if the corresponding pixel is reachable from previous references(see Appendix [A.3](https://arxiv.org/html/2510.01686v1#A1.SS3)). Masked attention $\mathcal{A}_{Masked}(\cdot)$ is implemented to incorporate $M_{Ref}$:

$$ $\mathbf{Out}_{1}^{s}=\mathcal{A}_{Masked}(\mathbf{Q}^{s},\mathbf{K}^{s}\oplus\mathbf{K}_{R}^{s},\mathbf{V}^{s}\oplus\mathbf{V}_{R}^{s},M_{Ref})\oplus\mathcal{A}(\mathbf{Q}^{s}_{R},\mathbf{K}^{s}\oplus\mathbf{K}_{R}^{s},\mathbf{V}^{s}\oplus\mathbf{V}_{R}^{s}),$ (5) $$

where $\{\mathbf{Q}^{s},\mathbf{K}^{s}\}$ and $\{\mathbf{Q}^{s}_{R},\mathbf{K}^{s}_{R}\}$ refer to the Query and Key matrices projected by $\mathbf{x}^{s}$ and $\mathbf{x}^{s}_{R}$. Inspired by prior UNet-based approaches that forward convolution and attention features from reconstruction to editing branches, we leverage QK-Sharing of the reconstruction queries and keys to enable more flexible attention operations in the stylization branch.
These queries and keys define spatial-temporal correspondences across the content video, which are critical for effective style propagation and temporal consistency. Thus, we replace the query and value in Equation [5](https://arxiv.org/html/2510.01686v1#S3.E5):

$$ $\mathbf{Out}_{2}^{s}=\mathcal{A}_{Masked}(\mathbf{Q}^{r},\mathbf{K}^{r}\oplus\mathbf{K}_{R}^{r},\mathbf{V}^{s}\oplus\mathbf{V}_{R}^{s},M_{Ref})\oplus\mathcal{A}(\mathbf{Q}^{r}_{R},\mathbf{K}^{r}\oplus\mathbf{K}_{R}^{r},\mathbf{V}^{s}\oplus\mathbf{V}_{R}^{s})$ (6) $$

Due to the auto-causal nature of the base model, the style of the generated video is primarily determined by the first reference frame, while subsequent references act as supplementary sources.

### 3.3 Explicit Optical Flow Guidance

When the camera or object motion is significant, we observe disappearing or time-varying style textures in plain areas. We attribute this issue to inaccurate attention maps between temporally distant frames, especially in regions with little salient visual features, as discussed in Section [2.2](https://arxiv.org/html/2510.01686v1#S2.SS2). To address this, we introduce Explicit Optical-flow Guidance (EOG), which provides pixel-level correspondence via optical flow. We first compute forward and backward optical flows and trace every pixel across frames. If a pixel $p_{i,j}^{s}$ in frame $s$ maps to location $p_{m,n}^{t}$ in frame $t$, the flow mask $M_{Flow}\in\{0,1\}^{T\times h\times w\times T\times h\times w}$ is set to True at index $(s,i,j,t,m,n)$ (see Appendix [A.4](https://arxiv.org/html/2510.01686v1#A1.SS4)). We further apply dilation to compensate for estimation errors of the optical flow.

Using this mask, we perform masked attention on stylization and corresponding reference tokens:

$$ $\mathbf{Out}_{3}^{s}=\mathcal{A}_{Masked}(\mathbf{Q}^{s}\oplus\mathbf{Q}_{R}^{s},\mathbf{K}^{s}\oplus\mathbf{K}_{R}^{s},\mathbf{V}^{s}\oplus\mathbf{V}_{R}^{s},M_{Flow}\land M_{Ref}),$ (7) $$

where $\land$ denotes a logical AND, ensuring that only consistent regions across flow and reference masks contribute. This design constrains the attention area of the source pixel on every frame, explicitly controlling the appearance to follow the video dynamics. Empirically, we find that using queries and keys from stylization tokens outperforms those from the reconstruction branch. Finally, the outputs from all three attention modes are aggregated before cross-attention as:

$$ $\mathbf{Out}^{s}=(1-\beta-\gamma)\cdot\mathbf{Out}_{1}^{s}+\beta\cdot\mathbf{Out}_{2}^{s}+\gamma\cdot\mathbf{Out}_{3}^{s},$ (8) $$

where $\beta$ and $\gamma$ are two hyperparameters, controlling the impact of the inconsistent references and the strength of the explicit appearance guidance. $\gamma$ is set to a non-zero value only in the final stage of the denoising process, when the model primarily focuses on local texture refinement.

Cross-Attention. The base model further encodes the reference image using CLIP Radford et al. (2021) to provide high-level semantic guidance via cross-attention. To incorporate multiple references, we concatenate the CLIP features from all reference frames and apply QK-Sharing in the cross-attention layer, enhancing the injection of language-aligned information. As CLIP features contain limited spatial structure, reference and flow masks are not applied.

Reference Arrangement. In principle, all the frames can be stylized and used as references in FreeViS for comprehensive style coverage. Nevertheless, increasing the number of references significantly raises computational and memory costs. Considering that most advanced I2V models are limited to short videos (typically around 81 frames), we select the first, middle, and last frames as references, an empirically effective choice for achieving high-quality stylization in most cases. Each reference token is assigned the same positional embedding as its corresponding frame latent to guarantee correct spatial and temporal propagation of stylized features.

## 4 Experiments

**Table 1: Quantitative results for video stylization. ”SC”, ”MS”, ”FC”, and ”HP” denote Style Consistency, Motion Smoothness, Flow Consistency, and Human Preference, respectively. We use the stylized references and original video to anchor the quality of stylization and video consistency.**
| Method | Stylization Quality | Video Consistency | HP $\uparrow$ |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| CSD Score $\uparrow$ | ArtFID $\downarrow$ | FID $\downarrow$ | LPIPS $\downarrow$ | SC $\uparrow$ | MS $\uparrow$ | FC $\downarrow$ |  |  |
| \rowcolorblue!8<br>Reference | 0.508 | 31.62 | 20.28 | 0.486 | 0.918 | 0.986 | 0.000 | - |
| TokenFlow | 0.111 | 37.87 | 27.94 | 0.309 | 0.915 | 0.976 | 1.092 | 2.179 |
| VACE | 0.138 | 35.53 | 27.77 | 0.240 | 0.910 | 0.984 | 0.554 | 2.895 |
| \rowcolorgray!10<br>I2VEdit | 0.331 | 38.72 | 22.53 | 0.653 | 0.738 | 0.975 | 2.074 | 2.538 |
| \rowcolorgray!10<br>AnyV2V | 0.267 | 35.84 | 23.52 | 0.471 | 0.753 | 0.961 | 1.715 | 2.443 |
| \rowcolorgray!10<br>AnyV2V* | 0.270 | 34.81 | 27.59 | 0.218 | 0.675 | 0.983 | 1.103 | 3.372 |
| \rowcolorgreen!8<br>Ours | 0.448 | 33.46 | 21.62 | 0.479 | 0.898 | 0.978 | 0.641 | 4.113 |

This section is divided into two major parts: Video Style Transfer and Stylized T2V Generation. All the stylized references for FreeViS and other reference-based video editing models are provided by InstantStyle-plus Wang et al. (2024b). We deliver the results of FreeViS combined with various image stylization methods and different video diffusion base models in Appendix [A.10](https://arxiv.org/html/2510.01686v1#A1.SS10) and [A.12](https://arxiv.org/html/2510.01686v1#A1.SS12).

### 4.1 Video Style Transfer

Dataset & Metrics.
We curated 200 online videos covering human activities, natural and urban scenes, animals, and transportation. Style images were sourced from WikiArt Tan et al. (2018) and web collections. For evaluation, we use the CSD Score Somepalli et al. (2024) to assess frame–style similarity, and ArtFID Wright & Ommer (2022) to jointly evaluate stylization quality and content preservation via FID and LPIPS. Motion smoothness is measured with VBench Huang et al. (2024), and optical flow consistency between the original and stylized videos with End-Point Error (EPE) Barron et al. (1994). Finally, we introduce Style Consistency, which quantifies the similarity between the first frame and subsequent frames based on style features extracted by Somepalli et al. (2024). We conduct a human preference evaluation to assess both the visual plausibility and the stylization quality of the generated videos. Each examiner is asked to assign a score from 1 to 5 for both aspects, and the final rating is obtained by averaging the two scores.

Figure: Figure 4: Qualitative comparison of FreeViS with other video editing methods on video stylization. The areas inside the bounding boxes show missing style textures and incorrect reconstruction.
Refer to caption: x4.png

Figure: Figure 5: Qualitative comparison of FreeViS with previous video and image stylization methods. The flickering issue of the image stylization methods can be observed in the supplemented video.
Refer to caption: x5.png

Baselines.
We compare against various baselines: reference-based video editing (AnyV2V Ku et al. (2024), I2VEdit Ouyang et al. (2024)), text-driven editing (TokenFlow Geyer et al. (2024)), unified video editing (VACE Jiang et al. (2025)), classic video style transfer (ReReVST Wang et al. (2020), ViSt3D Pande & Sharma (2023)), and image style transfer with temporal smoothing Duan et al. (2023) (AdaAttn Liu et al. (2021), InstantStyle-plus Wang et al. (2024b)).
Since the code for V2V implementation of StyleMaster Ye et al. (2025) has not been released, we omit its results currently. We re-implement AnyV2V, named by AnyV2V*, using the same base model as FreeViS for a fair comparison. Note that among all the models, only AnyV2V and TokenFlow are training-free.

Results.
Qualitative comparisons are presented in Figures [4](https://arxiv.org/html/2510.01686v1#S4.F4) and [5](https://arxiv.org/html/2510.01686v1#S4.F5). VACE demonstrates strong video consistency (Table [1](https://arxiv.org/html/2510.01686v1#S4.T1)) but is limited to color modification, struggling to capture and transfer complex style textures. Similarly, TokenFlow faces challenges in style representation, primarily due to the limitations of text-based descriptions, which parallel the issues observed with VACE. The original AnyV2V and I2VEdit models fail to reconstruct the video when there are substantial content changes between the first and last frames (see Appendix [A.11](https://arxiv.org/html/2510.01686v1#A1.SS11)), likely due to the weak priors of their pre-trained base models. While AnyV2V* significantly mitigates this issue, it still suffers from propagation errors: early-frame styles are not consistently transferred to later frames, resulting in the lowest style consistency score. In contrast, FreeViS effectively preserves stylistic features across the entire sequence, achieving the highest stylization quality and near-optimal temporal consistency.

Classical video stylization methods, which typically rely on non-diffusion architectures, offer strong temporal consistency but suffer from limited stylization quality. In contrast, image-based stylization methods, while capable of richer stylization, exhibit significant frame-wise inconsistencies. This leads to over-flattened style textures and persistent flickering artifacts, even after temporal smoothing. FreeViS overcomes these limitations by leveraging a pretrained video diffusion framework that effectively balances high-quality stylization with robust temporal coherence.

Figure: Figure 6: Qualitative comparison of FreeViS with previous methods for stylized video generation. The difference can be easily observed in the supplemented video.
Refer to caption: x6.png

**Table 2: Quantitative results for stylized video generation. ”DQ”, ”MS”, ”BC”, ”IQ”, and ”HP” denote Dynamic Quality, Motion Smoothness, Background (CLIP) Consistency, Imaging Quality, and Human Preference, respectively. A smaller DQ can naturally lead to better MS and BC.**
| Method | Stylization Quality | Video Generation Quality | HP $\uparrow$ |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| CSD Score $\uparrow$ | FID $\downarrow$ | CLIP-Text $\uparrow$ | DQ $\uparrow$ | MS $\uparrow$ | BC $\uparrow$ | IQ $\uparrow$ |  |  |
| \rowcolorgray!10<br>StyleCrafter | 0.515 | 22.62 | 0.211 | 0.368 | 0.965 | 0.951 | 0.578 | 2.83 |
| \rowcolorgray!10<br>StyleMaster | 0.221 | 26.04 | 0.243 | 0.123 | 0.985 | 0.945 | 0.667 | 2.55 |
| \rowcolorgreen!8<br>Ours+Wan | 0.437 | 24.63 | 0.264 | 0.509 | 0.980 | 0.941 | 0.691 | 3.97 |

### 4.2 Stylized T2V Generation

Dataset & Metrics & Baselines.
We utilize Qwen3 Yang et al. (2025a) to generate 200 text prompts describing the content of target videos, covering a broad range of commonly encountered real-world scenarios. The style images used are identical to those described in the previous section. As no ground-truth videos are available, we adopt four evaluation metrics from VBench Huang et al. (2024): Dynamic Quality, Motion Smoothness, Background Consistency, and Imaging Quality, to assess the visual quality of the generated videos. Additionally, CLIP-Text similarity is employed to evaluate video-prompt alignment. For assessing style consistency and fidelity, we report the CSD Score and FID, which measure the similarity between video frames and style images. Human preference is also employed to assess the prompt alignment, stylization quality, and visual plausibility. We compare our approach against two state-of-the-art baselines Liu et al. (2024a); Ye et al. (2025): StyleCrafter and StyleMaster. For stylized T2V generation, we first generate base videos using Wan2.1 Wan et al. (2025), followed by applying FreeViS for video stylization.

Results.
The qualitative and quantitative results for stylized video generation are presented in Figure [6](https://arxiv.org/html/2510.01686v1#S4.F6) and Table [2](https://arxiv.org/html/2510.01686v1#S4.T2). StyleCrafter effectively transfers style into the generated videos; however, its outputs often lack dynamic realism and occasionally fail to align with the input prompts. In contrast, StyleMaster better adheres to the textual prompts but exhibits limited stylization capability. The combination of FreeViS and the Wan model achieves the best trade-off between stylization fidelity and content alignment, producing videos that are both visually compelling and semantically accurate, as reflected by the highest human preference scores. Note that the stylization performance of FreeViS is inherently upper-bounded by the underlying image style transfer method it employs.

Figure: Figure 7: Ablation study of FreeViS. The upper images are generated from the full model.
Refer to caption: x7.png

### 4.3 Ablation Study

FreeViS primarily comprises three key components: IHC for layout reconstruction, Extra References for enforcing style consistency, and EOG for texture preservation in plain areas. The effectiveness of each component is illustrated in Figure [7](https://arxiv.org/html/2510.01686v1#S4.F7). Without IHC, the model fails to accurately reconstruct detailed scene layouts, leading to structural artifacts. Incorporating additional reference frames helps maintain consistent style features throughout the video, significantly enhancing visual coherence. Furthermore, the EOG module mitigates the loss of style textures in visually homogeneous regions of the original video, thereby improving flow consistency and overall stylization quality.

## 5 Related Work

Video Diffusion.
Diffusion-based video generation is advancing rapidly, with significant progress in quality Agarwal et al. (2025); Yang et al. (2025c); Blattmann et al. (2023), efficiency Xi et al. (2025); Zhang et al. (2025a); Kahatapitiya et al. (2024), controllability Zhang et al. (2025b); Bai et al. (2025a); Ren et al. (2025), and extrapolation Zhao et al. (2025); Dalal et al. (2025); Mao et al. (2024). Unlike early UNet-based architectures Blattmann et al. (2023); Guo et al. (2024); Mei & Patel (2023), which employ separate layers to model spatial and temporal dependencies, recent advances Kong et al. (2024); Yang et al. (2025c); Wan et al. (2025) adopt the full self-attention layers of DiT Peebles & Xie (2023) to jointly capture pixel-level coherence.

Video Editing.
Leveraging the strong priors of video diffusion models, recent editing methods have significantly outperformed earlier approaches based on optical flow Xu et al. (2019), GANs Tzaban et al. (2022); Frühstück et al. (2023), and image diffusion Ceylan et al. (2023); Geyer et al. (2024); Feng et al. (2024). Unlike image editing, which mainly enforces spatial coherence Brooks et al. (2023a; b), video editing additionally requires preserving temporal consistency Yang et al. (2025b). Prior work has addressed diverse tasks such as inpainting Bian et al. (2025); Guo et al. (2025), content swapping Liu et al. (2024b); Tu et al. (2025); Zhang et al. (2025c), and interactive control Deng et al. (2024); Ma et al. (2025). VACE Jiang et al. (2025) proposes a unified framework for video generation and editing with a mask-guided condition unit, while AnyV2V Ku et al. (2024) and I2VEdit Ouyang et al. (2024) leverage the first edited frame as guidance for pre-trained I2V models.

Style Transfer.
Neural style transfer Gatys et al. (2016) has progressed with deep architectures Huang & Belongie (2017); Liu et al. (2021); Deng et al. (2022) and generative models Azadi et al. (2018); Zhang et al. (2023); Wang et al. (2023). Diffusion models further improve semantic alignment between style and content Wang et al. (2024b); Xing et al. (2024), inspiring training-free methods Wang et al. (2024a); Rout et al. (2025); Chung et al. (2024); Xu et al. (2025b) that surpass traditional techniques. By contrast, video stylization remains underexplored. ViSt3D Pande & Sharma (2023) employs 3D CNNs for temporal consistency, StyleMaster Ye et al. (2025) finetunes a video diffusion model with CLIP-guided cross-attention, and StyleCrafter Liu et al. (2024a) enables style-conditioned text-to-video generation. Nonetheless, these approaches still lag behind image style transfer in quality, with temporal consistency being a key challenge.

## 6 Conclusion

In this paper, we introduce a training-free video stylization framework, FreeViS, which successfully addresses the propagation errors inherent in prior works. FreeViS leverages IHC to constrain the layout and motion. To enable additional reference inputs without requiring extra fine-tuning, we propose isolated attention with masking and dynamics injection. Furthermore, we propose the EOG strategy to preserve style textures in plain areas. Extensive experiments are conducted to validate the effectiveness of our design and the superior performance of FreeViS over other methods.

## References

- Agarwal et al. (2025)
Niket Agarwal, Arslan Ali, Maciej Bala, Yogesh Balaji, Erik Barker, Tiffany Cai, Prithvijit Chattopadhyay, Yongxin Chen, Yin Cui, Yifan Ding, et al.
Cosmos world foundation model platform for physical ai.
*arXiv preprint arXiv:2501.03575*, 2025.
- Azadi et al. (2018)
Samaneh Azadi, Matthew Fisher, Vladimir G Kim, Zhaowen Wang, Eli Shechtman, and Trevor Darrell.
Multi-content gan for few-shot font style transfer.
In *Proceedings of the IEEE conference on computer vision and pattern recognition*, pp. 7564–7573, 2018.
- Bai et al. (2025a)
Jianhong Bai, Menghan Xia, Xiao Fu, Xintao Wang, Lianrui Mu, Jinwen Cao, Zuozhu Liu, Haoji Hu, Xiang Bai, Pengfei Wan, et al.
Recammaster: Camera-controlled generative rendering from a single video.
*arXiv preprint arXiv:2503.11647*, 2025a.
- Bai et al. (2025b)
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang, Haiyang Xu, and Junyang Lin.
Qwen2.5-vl technical report, 2025b.
URL [https://arxiv.org/abs/2502.13923](https://arxiv.org/abs/2502.13923).
- Barron et al. (1994)
John L Barron, David J Fleet, and Steven S Beauchemin.
Performance of optical flow techniques.
*International journal of computer vision*, 12(1):43–77, 1994.
- Bian et al. (2025)
Yuxuan Bian, Zhaoyang Zhang, Xuan Ju, Mingdeng Cao, Liangbin Xie, Ying Shan, and Qiang Xu.
Videopainter: Any-length video inpainting and editing with plug-and-play context control.
In *Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers*, pp. 1–12, 2025.
- Blattmann et al. (2023)
Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al.
Stable video diffusion: Scaling latent video diffusion models to large datasets.
*arXiv preprint arXiv:2311.15127*, 2023.
- Brooks et al. (2023a)
Tim Brooks, Aleksander Holynski, and Alexei A. Efros.
Instructpix2pix: Learning to follow image editing instructions.
In *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)*, pp. 18392–18402, June 2023a.
- Brooks et al. (2023b)
Tim Brooks, Aleksander Holynski, and Alexei A Efros.
Instructpix2pix: Learning to follow image editing instructions.
In *Proceedings of the IEEE/CVF conference on computer vision and pattern recognition*, pp. 18392–18402, 2023b.
- Cao et al. (2023)
Mingdeng Cao, Xintao Wang, Zhongang Qi, Ying Shan, Xiaohu Qie, and Yinqiang Zheng.
Masactrl: Tuning-free mutual self-attention control for consistent image synthesis and editing.
In *Proceedings of the IEEE/CVF international conference on computer vision*, pp. 22560–22570, 2023.
- Ceylan et al. (2023)
Duygu Ceylan, Chun-Hao P Huang, and Niloy J Mitra.
Pix2video: Video editing using image diffusion.
In *Proceedings of the IEEE/CVF International Conference on Computer Vision*, pp. 23206–23217, 2023.
- Chen et al. (2025)
Sili Chen, Hengkai Guo, Shengnan Zhu, Feihu Zhang, Zilong Huang, Jiashi Feng, and Bingyi Kang.
Video depth anything: Consistent depth estimation for super-long videos.
In *Proceedings of the Computer Vision and Pattern Recognition Conference*, pp. 22831–22840, 2025.
- Chung et al. (2024)
Jiwoo Chung, Sangeek Hyun, and Jae-Pil Heo.
Style injection in diffusion: A training-free approach for adapting large-scale diffusion models for style transfer.
In *Proceedings of the IEEE/CVF conference on computer vision and pattern recognition*, pp. 8795–8805, 2024.
- Dalal et al. (2025)
Karan Dalal, Daniel Koceja, Gashon Hussein, Jiarui Xu, Yue Zhao, Youjin Song, Shihao Han, Ka Chun Cheung, Jan Kautz, Carlos Guestrin, et al.
One-minute video generation with test-time training.
*arXiv preprint arXiv:2504.05298*, 2025.
- Dao (2024)
Tri Dao.
Flashattention-2: Faster attention with better parallelism and work partitioning.
In *The Twelfth International Conference on Learning Representations*, 2024.
URL [https://openreview.net/forum?id=mZn2Xyh9Ec](https://openreview.net/forum?id=mZn2Xyh9Ec).
- Deng et al. (2022)
Yingying Deng, Fan Tang, Weiming Dong, Chongyang Ma, Xingjia Pan, Lei Wang, and Changsheng Xu.
Stytr2: Image style transfer with transformers.
In *Proceedings of the IEEE/CVF conference on computer vision and pattern recognition*, pp. 11326–11336, 2022.
- Deng et al. (2024)
Yufan Deng, Ruida Wang, Yuhao Zhang, Yu-Wing Tai, and Chi-Keung Tang.
Dragvideo: Interactive drag-style video editing.
In *European Conference on Computer Vision*, pp. 183–199. Springer, 2024.
- Duan et al. (2023)
Zhongjie Duan, Chengyu Wang, Cen Chen, Weining Qian, Jun Huang, and Mingyi Jin.
Fastblend: a powerful model-free toolkit making video stylization easier.
*arXiv preprint arXiv:2311.09265*, 2023.
- Feng et al. (2024)
Ruoyu Feng, Wenming Weng, Yanhui Wang, Yuhui Yuan, Jianmin Bao, Chong Luo, Zhibo Chen, and Baining Guo.
Ccedit: Creative and controllable video editing via diffusion models.
In *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*, pp. 6712–6722, 2024.
- Frühstück et al. (2023)
Anna Frühstück, Nikolaos Sarafianos, Yuanlu Xu, Peter Wonka, and Tony Tung.
Vive3d: Viewpoint-independent video editing using 3d-aware gans.
In *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*, pp. 4446–4455, 2023.
- Gatys et al. (2016)
Leon A Gatys, Alexander S Ecker, and Matthias Bethge.
Image style transfer using convolutional neural networks.
In *Proceedings of the IEEE conference on computer vision and pattern recognition*, pp. 2414–2423, 2016.
- Geyer et al. (2024)
Michal Geyer, Omer Bar-Tal, Shai Bagon, and Tali Dekel.
Tokenflow: Consistent diffusion features for consistent video editing.
In *The Twelfth International Conference on Learning Representations*, 2024.
URL [https://openreview.net/forum?id=lKK50q2MtV](https://openreview.net/forum?id=lKK50q2MtV).
- Guo et al. (2024)
Yuwei Guo, Ceyuan Yang, Anyi Rao, Zhengyang Liang, Yaohui Wang, Yu Qiao, Maneesh Agrawala, Dahua Lin, and Bo Dai.
Animatediff: Animate your personalized text-to-image diffusion models without specific tuning.
In *The Twelfth International Conference on Learning Representations*, 2024.
URL [https://openreview.net/forum?id=Fx2SbBgcte](https://openreview.net/forum?id=Fx2SbBgcte).
- Guo et al. (2025)
Yuwei Guo, Ceyuan Yang, Anyi Rao, Chenlin Meng, Omer Bar-Tal, Shuangrui Ding, Maneesh Agrawala, Dahua Lin, and Bo Dai.
Keyframe-guided creative video inpainting.
In *Proceedings of the Computer Vision and Pattern Recognition Conference*, pp. 13009–13020, 2025.
- Hertz et al. (2022)
Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or.
Prompt-to-prompt image editing with cross attention control.
*arXiv preprint arXiv:2208.01626*, 2022.
- Ho et al. (2020)
Jonathan Ho, Ajay Jain, and Pieter Abbeel.
Denoising diffusion probabilistic models.
*Advances in neural information processing systems*, 33:6840–6851, 2020.
- Huang & Belongie (2017)
Xun Huang and Serge Belongie.
Arbitrary style transfer in real-time with adaptive instance normalization.
In *Proceedings of the IEEE international conference on computer vision*, pp. 1501–1510, 2017.
- Huang et al. (2024)
Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, et al.
Vbench: Comprehensive benchmark suite for video generative models.
In *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*, pp. 21807–21818, 2024.
- Jiang et al. (2025)
Zeyinzi Jiang, Zhen Han, Chaojie Mao, Jingfeng Zhang, Yulin Pan, and Yu Liu.
Vace: All-in-one video creation and editing.
*arXiv preprint arXiv:2503.07598*, 2025.
- Ju et al. (2024)
Xuan Ju, Ailing Zeng, Yuxuan Bian, Shaoteng Liu, and Qiang Xu.
Pnp inversion: Boosting diffusion-based editing with 3 lines of code.
In *The Twelfth International Conference on Learning Representations*, 2024.
URL [https://openreview.net/forum?id=FoMZ4ljhVw](https://openreview.net/forum?id=FoMZ4ljhVw).
- Kahatapitiya et al. (2024)
Kumara Kahatapitiya, Haozhe Liu, Sen He, Ding Liu, Menglin Jia, Chenyang Zhang, Michael S Ryoo, and Tian Xie.
Adaptive caching for faster video generation with diffusion transformers.
*arXiv preprint arXiv:2411.02397*, 2024.
- Kong et al. (2024)
Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu, Jianwei Zhang, et al.
Hunyuanvideo: A systematic framework for large video generative models.
*arXiv preprint arXiv:2412.03603*, 2024.
- Ku et al. (2024)
Max Ku, Cong Wei, Weiming Ren, Harry Yang, and Wenhu Chen.
Anyv2v: A tuning-free framework for any video-to-video editing tasks.
*arXiv preprint arXiv:2403.14468*, 2024.
- Li et al. (2024)
Xirui Li, Chao Ma, Xiaokang Yang, and Ming-Hsuan Yang.
Vidtome: Video token merging for zero-shot video editing.
In *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*, pp. 7486–7495, 2024.
- Lipman et al. (2023)
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matthew Le.
Flow matching for generative modeling.
In *The Eleventh International Conference on Learning Representations*, 2023.
URL [https://openreview.net/forum?id=PqvMRDCJT9t](https://openreview.net/forum?id=PqvMRDCJT9t).
- Liu et al. (2024a)
Gongye Liu, Menghan Xia, Yong Zhang, Haoxin Chen, Jinbo Xing, Yibo Wang, Xintao Wang, Ying Shan, and Yujiu Yang.
Stylecrafter: Taming artistic video diffusion with reference-augmented adapter learning.
*ACM Trans. Graph.*, 2024a.
- Liu et al. (2024b)
Shaoteng Liu, Yuechen Zhang, Wenbo Li, Zhe Lin, and Jiaya Jia.
Video-p2p: Video editing with cross-attention control.
In *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*, pp. 8599–8608, 2024b.
- Liu et al. (2021)
Songhua Liu, Tianwei Lin, Dongliang He, Fu Li, Meiling Wang, Xin Li, Zhengxing Sun, Qian Li, and Errui Ding.
Adaattn: Revisit attention mechanism in arbitrary neural style transfer.
In *Proceedings of the IEEE/CVF international conference on computer vision*, pp. 6649–6658, 2021.
- Ma et al. (2025)
Yue Ma, Xiaodong Cun, Sen Liang, Jinbo Xing, Yingqing He, Chenyang Qi, Siran Chen, and Qifeng Chen.
Magicstick: Controllable video editing via control handle transformations.
In *2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)*, pp. 9385–9395. IEEE, 2025.
- Mao et al. (2024)
Jiawei Mao, Xiaoke Huang, Yunfei Xie, Yuanqi Chang, Mude Hui, Bingjie Xu, and Yuyin Zhou.
Story-adapter: A training-free iterative framework for long story visualization.
*arXiv preprint arXiv:2410.06244*, 2024.
- Mei & Patel (2023)
Kangfu Mei and Vishal Patel.
Vidm: video implicit diffusion models.
In *Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence*, AAAI’23/IAAI’23/EAAI’23. AAAI Press, 2023.
ISBN 978-1-57735-880-0.
doi: 10.1609/aaai.v37i8.26094.
URL [https://doi.org/10.1609/aaai.v37i8.26094](https://doi.org/10.1609/aaai.v37i8.26094).
- Ouyang et al. (2024)
Wenqi Ouyang, Yi Dong, Lei Yang, Jianlou Si, and Xingang Pan.
I2vedit: First-frame-guided video editing via image-to-video diffusion models.
In *SIGGRAPH Asia 2024 Conference Papers*, pp. 1–11, 2024.
- Pande & Sharma (2023)
Ayush Pande and Gaurav Sharma.
Vist3d: video stylization with 3d cnn.
*Advances in Neural Information Processing Systems*, 36:41651–41662, 2023.
- Parmar et al. (2023)
Gaurav Parmar, Krishna Kumar Singh, Richard Zhang, Yijun Li, Jingwan Lu, and Jun-Yan Zhu.
Zero-shot image-to-image translation.
In *ACM SIGGRAPH 2023 conference proceedings*, pp. 1–11, 2023.
- Peebles & Xie (2023)
William Peebles and Saining Xie.
Scalable diffusion models with transformers.
In *Proceedings of the IEEE/CVF international conference on computer vision*, pp. 4195–4205, 2023.
- Radford et al. (2021)
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al.
Learning transferable visual models from natural language supervision.
In *International conference on machine learning*, pp. 8748–8763. PmLR, 2021.
- Ren et al. (2025)
Xuanchi Ren, Tianchang Shen, Jiahui Huang, Huan Ling, Yifan Lu, Merlin Nimier-David, Thomas Müller, Alexander Keller, Sanja Fidler, and Jun Gao.
Gen3c: 3d-informed world-consistent video generation with precise camera control.
*arXiv preprint arXiv:2503.03751*, 2025.
- Rout et al. (2025)
Litu Rout, Yujia Chen, Nataniel Ruiz, Abhishek Kumar, Constantine Caramanis, Sanjay Shakkottai, and Wen-Sheng Chu.
Rb-modulation: Training-free stylization using reference-based modulation.
In *The Thirteenth International Conference on Learning Representations*, 2025.
- Siméoni et al. (2025)
Oriane Siméoni, Huy V Vo, Maximilian Seitzer, Federico Baldassarre, Maxime Oquab, Cijo Jose, Vasil Khalidov, Marc Szafraniec, Seungeun Yi, Michaël Ramamonjisoa, et al.
Dinov3.
*arXiv preprint arXiv:2508.10104*, 2025.
- Somepalli et al. (2024)
Gowthami Somepalli, Anubhav Gupta, Kamal Gupta, Shramay Palta, Micah Goldblum, Jonas Geiping, Abhinav Shrivastava, and Tom Goldstein.
Measuring style similarity in diffusion models.
*arXiv preprint arXiv:2404.01292*, 2024.
- Staniszewski et al. (2024)
Łukasz Staniszewski, Łukasz Kuciński, and Kamil Deja.
There and back again: On the relation between noise and image inversions in diffusion models.
*arXiv preprint arXiv:2410.23530*, 2024.
- Tan et al. (2018)
Wei Ren Tan, Chee Seng Chan, Hernan E Aguirre, and Kiyoshi Tanaka.
Improved artgan for conditional synthesis of natural image and artwork.
*IEEE Transactions on Image Processing*, 28(1):394–409, 2018.
- Teed & Deng (2020)
Zachary Teed and Jia Deng.
Raft: Recurrent all-pairs field transforms for optical flow.
In *European conference on computer vision*, pp. 402–419. Springer, 2020.
- Tu et al. (2025)
Yuanpeng Tu, Hao Luo, Xi Chen, Sihui Ji, Xiang Bai, and Hengshuang Zhao.
Videoanydoor: High-fidelity video object insertion with precise motion control.
In *Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers*, pp. 1–11, 2025.
- Tzaban et al. (2022)
Rotem Tzaban, Ron Mokady, Rinon Gal, Amit Bermano, and Daniel Cohen-Or.
Stitch it in time: Gan-based facial editing of real videos.
In *SIGGRAPH Asia 2022 Conference Papers*, pp. 1–9, 2022.
- Wan et al. (2025)
Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, et al.
Wan: Open and advanced large-scale video generative models.
*arXiv preprint arXiv:2503.20314*, 2025.
- Wang et al. (2024a)
Haofan Wang, Matteo Spinelli, Qixun Wang, Xu Bai, Zekui Qin, and Anthony Chen.
Instantstyle: Free lunch towards style-preserving in text-to-image generation.
*arXiv preprint arXiv:2404.02733*, 2024a.
- Wang et al. (2024b)
Haofan Wang, Peng Xing, Renyuan Huang, Hao Ai, Qixun Wang, and Xu Bai.
Instantstyle-plus: Style transfer with content-preserving in text-to-image generation.
*arXiv preprint arXiv:2407.00788*, 2024b.
- Wang et al. (2020)
Wenjing Wang, Shuai Yang, Jizheng Xu, and Jiaying Liu.
Consistent video style transfer via relaxation and regularization.
*IEEE Transactions on Image Processing*, 29:9125–9139, 2020.
- Wang et al. (2023)
Zhizhong Wang, Lei Zhao, and Wei Xing.
Stylediffusion: Controllable disentangled style transfer via diffusion models.
In *Proceedings of the IEEE/CVF international conference on computer vision*, pp. 7677–7689, 2023.
- Wright & Ommer (2022)
Matthias Wright and Björn Ommer.
Artfid: Quantitative evaluation of neural style transfer.
In *DAGM German Conference on Pattern Recognition*, pp. 560–576. Springer, 2022.
- Xi et al. (2025)
Haocheng Xi, Shuo Yang, Yilong Zhao, Chenfeng Xu, Muyang Li, Xiuyu Li, Yujun Lin, Han Cai, Jintao Zhang, Dacheng Li, et al.
Sparse videogen: Accelerating video diffusion transformers with spatial-temporal sparsity.
*arXiv preprint arXiv:2502.01776*, 2025.
- Xing et al. (2024)
Peng Xing, Haofan Wang, Yanpeng Sun, Qixun Wang, Xu Bai, Hao Ai, Renyuan Huang, and Zechao Li.
Csgo: Content-style composition in text-to-image generation.
*arXiv preprint arXiv:2408.16766*, 2024.
- Xu et al. (2025a)
Pengcheng Xu, Boyuan Jiang, Xiaobin Hu, Donghao Luo, Qingdong He, Jiangning Zhang, Chengjie Wang, Yunsheng Wu, Charles Ling, and Boyu Wang.
Unveil inversion and invariance in flow transformer for versatile image editing.
In *Proceedings of the Computer Vision and Pattern Recognition Conference*, pp. 28479–28489, 2025a.
- Xu et al. (2019)
Rui Xu, Xiaoxiao Li, Bolei Zhou, and Chen Change Loy.
Deep flow-guided video inpainting.
In *Proceedings of the IEEE/CVF conference on computer vision and pattern recognition*, pp. 3723–3732, 2019.
- Xu et al. (2025b)
Ruojun Xu, Weijie Xi, XiaoDi Wang, Yongbo Mao, and Zach Cheng.
Stylessp: Sampling startpoint enhancement for training-free diffusion-based method for style transfer.
In *Proceedings of the Computer Vision and Pattern Recognition Conference*, pp. 18260–18269, 2025b.
- Yang et al. (2025a)
An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al.
Qwen3 technical report.
*arXiv preprint arXiv:2505.09388*, 2025a.
- Yang et al. (2025b)
Xiangpeng Yang, Linchao Zhu, Hehe Fan, and Yi Yang.
Videograin: Modulating space-time attention for multi-grained video editing.
In *The Thirteenth International Conference on Learning Representations*, 2025b.
URL [https://openreview.net/forum?id=SSslAtcPB6](https://openreview.net/forum?id=SSslAtcPB6).
- Yang et al. (2023)
Xingyi Yang, Daquan Zhou, Jiashi Feng, and Xinchao Wang.
Diffusion probabilistic model made slim.
In *Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition*, pp. 22552–22562, 2023.
- Yang et al. (2025c)
Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, Da Yin, Yuxuan.Zhang, Weihan Wang, Yean Cheng, Bin Xu, Xiaotao Gu, Yuxiao Dong, and Jie Tang.
Cogvideox: Text-to-video diffusion models with an expert transformer.
In *The Thirteenth International Conference on Learning Representations*, 2025c.
URL [https://openreview.net/forum?id=LQzN6TRFg9](https://openreview.net/forum?id=LQzN6TRFg9).
- Ye et al. (2025)
Zixuan Ye, Huijuan Huang, Xintao Wang, Pengfei Wan, Di Zhang, and Wenhan Luo.
Stylemaster: Stylize your video with artistic generation and translation.
In *Proceedings of the Computer Vision and Pattern Recognition Conference*, pp. 2630–2640, 2025.
- Zhang et al. (2025a)
Shilong Zhang, Wenbo Li, Shoufa Chen, Chongjian Ge, Peize Sun, Yida Zhang, Yi Jiang, Zehuan Yuan, Binyue Peng, and Ping Luo.
Flashvideo: Flowing fidelity to detail for efficient high-resolution video generation.
*arXiv preprint arXiv:2502.05179*, 2025a.
- Zhang et al. (2025b)
Xu Zhang, Hao Zhou, Haoming Qin, Xiaobin Lu, Jiaxing Yan, Guanzhong Wang, Zeyu Chen, and Yi Liu.
Enabling versatile controls for video diffusion models.
*arXiv preprint arXiv:2503.16983*, 2025b.
- Zhang et al. (2025c)
Youyuan Zhang, Xuan Ju, and James J Clark.
Fastvideoedit: Leveraging consistency models for efficient text-to-video editing.
In *2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)*, pp. 3657–3666. IEEE, 2025c.
- Zhang et al. (2023)
Yuxin Zhang, Nisha Huang, Fan Tang, Haibin Huang, Chongyang Ma, Weiming Dong, and Changsheng Xu.
Inversion-based style transfer with diffusion models.
In *Proceedings of the IEEE/CVF conference on computer vision and pattern recognition*, pp. 10146–10156, 2023.
- Zhao et al. (2025)
Min Zhao, Guande He, Yixiao Chen, Hongzhou Zhu, Chongxuan Li, and Jun Zhu.
Riflex: A free lunch for length extrapolation in video diffusion transformers.
*arXiv preprint arXiv:2502.15894*, 2025.

## Appendix A Appendix

### A.1 Preliminary: PnP Inversion

Figure: Figure 8: Latent movement along the inversion and denoising process for reconstruction and editing. Left: Conventional Inversion; Right: PnP Inversion. Purple arrows refer to the compensation.
Refer to caption: x8.png

Different from conventional inversion-based editing methods, PnP inversion Ju et al. (2024) records the full denoising trajectory
$[\,\mathbf{x}_{t},\mathbf{x}_{t-1},\ldots,\mathbf{x}_{0}\,]$ (the *target latents*) during inversion, and explicitly injects the discrepancy between the reconstruction latent $\mathbf{x}^{r}_{t}$ and the target latent $\mathbf{x}_{t}$ into both the reconstruction and editing latents $\mathbf{x}^{e}_{t}$.
This compensation mitigates the accumulated error that is otherwise amplified by classifier-free guidance, thereby steering both reconstruction and editing back toward the original denoising trajectory (Figure [8](https://arxiv.org/html/2510.01686v1#A1.F8)).
The update rule can be expressed as:

$$ $\mathbf{x}^{r}_{t}\leftarrow\mathbf{x}^{r}_{t}+\big(\mathbf{x}_{t}-\mathbf{x}^{r}_{t}\big),\quad\mathbf{x}^{e}_{t}\leftarrow\mathbf{x}^{e}_{t}+\big(\mathbf{x}_{t}-\mathbf{x}^{r}_{t}\big)$ (9) $$

In this paper, we observe that the inverted noise alone is insufficient to guarantee the correct reconstruction of the layout and motion in modern video diffusion models, and PnP inversion (combined with our IHC strategy) can successfully resolve this issue.

### A.2 Implementation Details

Control signals such as depth, Canny edges, and tile images offer additional content guidance for generative models and have been widely adopted in both training-free and training-based style transfer approaches Xu et al. (2025b); Xing et al. (2024); Wang et al. (2024a); Ye et al. (2025). To enhance motion preservation, we adopt Wan2.1-Fun-Control as our pretrained base model, an adapted variant of Wan2.1 Wan et al. (2025) that can incorporate depth as a control signal. This base model positions the reference tokens at the beginning of the visual token sequence and supports only a single reference image, which serves as the initial frame of the generated video. One can freely change the base model to other video diffusion models, given the FreeViS framework. We utilize Euler Inversion with fixed-point iteration Xu et al. (2025a) to perform inversion under the Rectified Flow framework Lipman et al. (2023). The number of iterations can be adjusted based on the users’ computational budgets. During inversion, we omit the reference latent and provide only the video depth map parsed by Video Depth Anything Chen et al. (2025) and a concise text prompt extracted using Qwen2.5-VL Bai et al. (2025b), which describes the content and dynamics of the input video.

### A.3 Computation of Reference Masks

Let video frames be indexed by $t=0,\dots,T-1$ with pixel domain $\Omega_{t}\subset\mathbb{Z}^{2}$. We adopt RAFT Teed & Deng (2020) $\mathbf{f}_{t}:\mathbb{R}^{2}\to\mathbb{R}^{2}$ to extract the forward optical flow from frame $t$ to $t{+}1$, and let $\Pi:\mathbb{R}^{2}\to\Omega_{t}$ denote nearest-neighbor discretization to pixels.

$$ $\mathbf{p}_{s}=\mathbf{p},\quad\mathbf{p}_{k+1}=\mathbf{p}_{k}+\mathbf{f}_{k}\!\big(\mathbf{p}_{k}\big)\quad(k=s,\dots,t{-}1),\qquad\Phi_{s\to t}(\mathbf{p})=\mathbf{p}_{t}$ (10) $$

where $\Phi_{s\to t}$ denotes the forward propagation operator. Given a target frame $t$ and a source set $S\subseteq\{0,\dots,T{-}1\}$, the set of pixels in frame $t$ covered by $S$ is computed by:

$$ $\mathcal{C}_{t}(S)=\Big\{\;\Pi\!\big(\Phi_{a\to t}(\mathbf{u})\big)\;:\;\mathbf{u}\in\Omega_{a},\;a\in S\;\Big\}\subseteq\Omega_{t}$ (11) $$

With two given frame indices $i$ and $j$ ($0<i<j\leq T-1$), the two novel-region masks are:

$$ $M_{i}(\mathbf{p})=\mathds{1}\!\big[\mathbf{p}\notin\mathcal{C}_{i}(\{0\})\big],\qquad M_{j}(\mathbf{p})=\mathds{1}\!\big[\mathbf{p}\notin\mathcal{C}_{j}(\{0,i\})\big],\quad\mathbf{p}\in\Omega_{t}$ (12) $$

This approach can be extended to more references with a pre-defined order.

### A.4 Computation of Flow Masks

We construct a dense binary correspondence mask that records, for every source pixel in frame $s$, the (discretized) terminal location it reaches in any target frame $t$ when transported by optical flow. Let frames be indexed by $t=0,\dots,T{-}1$ with pixel domains $\Omega_{t}\subset\mathbb{Z}^{2}$. As in §[A.3](https://arxiv.org/html/2510.01686v1#A1.SS3), RAFT Teed & Deng (2020) provides forward flows $\mathbf{f}_{k}:\mathbb{R}^{2}\!\to\!\mathbb{R}^{2}$ from $k$ to $k{+}1$ (and analogously backward flows $\tilde{\mathbf{f}}_{k}$ from $k{+}1$ to $k$), and $\Pi_{t}:\mathbb{R}^{2}\!\to\!\Omega_{t}$ denotes nearest-neighbor discretization onto frame $t$.

#### Intermediate sampling and terminal validity.

Starting at $\mathbf{p}_{s}=\mathbf{u}\in\mathbb{R}^{2}$, we propagate *forward* from $s$ to $t\geq s$ by sampling the flow at the nearest valid pixel and updating the *continuous* state:

$$ $\hat{\mathbf{p}}_{k}=\Pi_{k}(\mathbf{p}_{k}),\qquad\mathbf{p}_{k+1}=\mathbf{p}_{k}+\mathbf{f}_{k}\!\big(\hat{\mathbf{p}}_{k}\big),\quad k=s,\dots,t{-}1,\qquad\Phi_{s\to t}(\mathbf{u})=\mathbf{p}_{t}$ (13) $$

The *backward* transport for $t<s$ is defined analogously using $\tilde{\mathbf{f}}_{k}$:

$$ $\hat{\mathbf{p}}_{k+1}=\Pi_{k+1}(\mathbf{p}_{k+1}),\qquad\mathbf{p}_{k}=\mathbf{p}_{k+1}+\tilde{\mathbf{f}}_{k}\!\big(\hat{\mathbf{p}}_{k+1}\big),\quad k=s{-}1,\dots,t,\qquad\tilde{\Phi}_{s\to t}(\mathbf{u})=\mathbf{p}_{t}$ (14) $$

Note that intermediate positions $\mathbf{p}_{k}$ are allowed to leave the image domain; $\Pi_{k}(\cdot)$ is used *only* to fetch subsequent flow values. We enforce validity *solely at the terminal point*: a trajectory from $s$ to $t$ is considered valid iff the continuous endpoint lies in $\Omega_{t}$.

#### Bidirectional map and landing index.

Combining equation [13](https://arxiv.org/html/2510.01686v1#A1.E13)–equation [14](https://arxiv.org/html/2510.01686v1#A1.E14), define the bidirectional transport

$$ $\Lambda_{s\to t}(\mathbf{u})\,=\,\begin{cases}\Phi_{s\to t}(\mathbf{u}),&t\geq s,\[2.0pt] \tilde{\Phi}_{s\to t}(\mathbf{u}),&t<s\end{cases}$ (15) $$

Given $\mathbf{u}\in\Omega_{s}$, its terminal continuous location is $\mathbf{q}=\Lambda_{s\to t}(\mathbf{u})$, and its discretized landing pixel is

$$ $\mathbf{v}^{\star}=\Pi_{t}\!\big(\mathbf{q}\big)\in\Omega_{t},\qquad\chi_{s\to t}(\mathbf{u})=\mathds{1}\!\big[\mathbf{q}\in\Omega_{t}\big]\in\{0,1\}$ (16) $$

#### Pairwise mask and tensor stacking.

The pairwise correspondence mask $M_{s\to t}:\Omega_{s}\times\Omega_{t}\to\{0,1\}$ places a single one at the discretized landing pixel when the terminal point is in-bounds:

$$ $M_{s\to t}(\mathbf{u},\mathbf{v})\;=\;\chi_{s\to t}(\mathbf{u})\;\mathds{1}\!\big[\mathbf{v}=\mathbf{v}^{\star}\big],\qquad\mathbf{u}\in\Omega_{s},\ \mathbf{v}\in\Omega_{t}$ (17) $$

Consequently, $\sum_{\mathbf{v}\in\Omega_{t}}M_{s\to t}(\mathbf{u},\mathbf{v})\in\{0,1\}$ for every $\mathbf{u}$, and the self-map reduces to the pixelwise identity: $M_{s\to s}(\mathbf{u},\mathbf{v})=\mathds{1}[\mathbf{v}=\mathbf{u}]$. Stacking $M_{s\to t}$ over all $(s,t)$ yields the rank-6 tensor

$$ $M\in\{0,1\}^{T\times h\times w\times T\times h\times w},\qquad M[s,y,x,t,y^{\prime},x^{\prime}]=M_{s\to t}\big((y,x),(y^{\prime},x^{\prime})\big)$ (18) $$

#### Remarks.

In practice we adopt nearest-neighbor sampling via $\Pi_{t}(\cdot)$ in equation [13](https://arxiv.org/html/2510.01686v1#A1.E13)–equation [14](https://arxiv.org/html/2510.01686v1#A1.E14); bilinear interpolation can be substituted without changing the definition in equation [17](https://arxiv.org/html/2510.01686v1#A1.E17) because the landing index is discretized by $\Pi_{t}(\cdot)$. Our construction matches the implementation choice that *intermediate* out-of-bounds positions are permitted, while *only* the *terminal* in-bounds condition $\chi_{s\to t}$ is enforced. Alternatively, the optical flow module can be replaced with other correspondence estimation methods, such as DINOv3 Siméoni et al. (2025) features, for improved performance.

### A.5 Dynamic–Appearance Decomposition for a Causal VAE

Figure: Figure 9: Optimal Offset and visualization for appearance transfer. (a) The plot for reconstruction PSNR with different offsets; (b) The RGB video and its reconstruction via appearance transfer.
Refer to caption: x9.png

The causal VAE in Wan Wan et al. (2025) encodes a video using a temporal subsampling factor $r$, producing a *time-compressed* latent sequence. We show that this video latent admits a decomposition into (i) an *appearance* term mainly determined by one frame per $r$-frame block and (ii) a *dynamic* residual. For $r=4$, the clip’s appearance is therefore dominated by $1/4$ of the frames.

Sampling and per-frame encoding.
Given an $N$-frame video $V=\{V[t]\}_{t=0}^{N-1}$ and an offset $\Delta p\in\{0,\dots,r\}$, define the sampled frame *sequence*

$$ $S_{r}(V,\Delta p)=\big(V[0]\big)\;\cup\;\big(V[rj+\Delta p]\,\big|\,j=0,1,\dots,\lfloor(N-1)/r\rfloor-1\big)$ $$

Let $\mathscr{E}_{\mathrm{vid}}$ be the video encoder and $\mathscr{E}_{\mathrm{img}}$ the same backbone in image (framewise) mode. The video encoder yields:

$$ $z(V)=\mathscr{E}_{\mathrm{vid}}(V)$ $$

The latents for sampled sequence is obtained by encoding the sampled frames *independently* and stacking them along time:

$$ $a(V,\Delta p)=\big(\,\mathscr{E}_{\mathrm{img}}(I)\,\big)_{I\in S_{r}(V,\Delta p)}$ $$

Because the sampled frames are independently encoded, $a(V,\Delta p)$ only contains appearance information. Define the dynamic residual by:

$$ $z_{\mathrm{dyn}}(V,\Delta p)=z(V)-a(V,\Delta p)$ $$

#### Appearance–dynamics exchange.

For an RGB clip $V_{\mathrm{rgb}}$ and its grayscale counterpart $V_{\mathrm{gray}}$, swapping appearance while preserving dynamics gives:

$$ $\displaystyle V_{\mathrm{rgb}}^{\prime}(\Delta p)$ $\displaystyle=\mathscr{D}\!\left(z_{\mathrm{dyn}}(V_{\mathrm{gray}},\Delta p)+a(V_{\mathrm{rgb}},\Delta p)\right),$ (19) $\displaystyle V_{\mathrm{gray}}^{\prime}(\Delta p)$ $\displaystyle=\mathscr{D}\!\left(z_{\mathrm{dyn}}(V_{\mathrm{rgb}},\Delta p)+a(V_{\mathrm{gray}},\Delta p)\right),$ (20) $$

where $\mathscr{D}$ denotes the VAE decoder. This implements “subtract source appearance, add target appearance” while keeping the dynamics fixed. Figure [9](https://arxiv.org/html/2510.01686v1#A1.F9)-(b) demonstrates that the appearance of the RGB video is successfully combined with grayscale dynamics, with only a minor color shift. This observation inspires us to inject the dynamic residual into the value matrices of static reference latents, which addresses the stuttering issue of additional reference inputs in Section [3.2](https://arxiv.org/html/2510.01686v1#S3.SS2).

#### Choosing the offset.

$\Delta p$ selects which frame within each $r$-frame block anchors appearance. We choose it by minimizing cross-reconstruction error:

$$ $\Delta p^{\star}=\arg\min_{\Delta p\in\{0,\dots,r\}}\mathcal{L}\!\left(V_{\mathrm{rgb}}^{\prime}(\Delta p),V_{\mathrm{rgb}}\right)+\mathcal{L}\!\left(V_{\mathrm{gray}}^{\prime}(\Delta p),V_{\mathrm{gray}}\right),$ $$

where $\mathcal{L}$ can be an $\ell_{1}$+LPIPS video reconstruction metric. As shown in Figure [9](https://arxiv.org/html/2510.01686v1#A1.F9)-(a), the reconstruction performance is optimal when $\Delta p=3$ for $r=4$. Furthermore, the PCA visualizations of the video latents and the sampled frames exhibit close alignment (Figure [10](https://arxiv.org/html/2510.01686v1#A1.F10)), indicating that the temporal anchoring is consistent across the two representations. The additional references should be selected from $S_{r}(V,\Delta p)$, and the optical flow for inconsistency masking and EOG in Section [3.2](https://arxiv.org/html/2510.01686v1#S3.SS2) should be calculated accordingly for optimal performance.

### A.6 Denoising Process of Wan I2V Model

Figure: Figure 10: Denoising process of Wan I2V model. We apply PCA to visualize the latents for different frames along the entire denoising process. The corresponding sampled video frames are given.
Refer to caption: x10.png

As in image diffusion models, the early denoising stages in video diffusion primarily establish the coarse global layout and motion of the generated video, while the later stages are dedicated to refining fine-grained details. As illustrated in Figure [10](https://arxiv.org/html/2510.01686v1#A1.F10), the primary layout and structure of the entire video are largely determined by around the 20th denoising step. In later steps, the model mainly focuses on the local appearance refinement. The PCA visualizations of the latents align well with our sampled frames with optimal offset.

Figure: Figure 11: Effect of noise initialization for video stylization with reference. (a) The generated videos share the same initial Gaussian noise. (b) Original video and its stylization via inversion.
Refer to caption: x11.png

### A.7 Noise Initialization

In recent diffusion-based image and video editing works Hertz et al. (2022); Cao et al. (2023); Parmar et al. (2023); Yang et al. (2025b); Li et al. (2024), noise inversion is a prerequisite step for essential content preservation when large-scale training is infeasible. However, we find that noise initialization alone is insufficient to capture the detailed motion and content for video stylization.

Gaussian noise is widely adopted as a standard initialization for generative models Ho et al. (2020); Lipman et al. (2023). Nevertheless, as shown in Figure [11](https://arxiv.org/html/2510.01686v1#A1.F11), while the stylized reference image retains the overall semantic layout, it is treated as an out-of-distribution example, and the video generated from the same Gaussian noise fails to exhibit realistic dynamic motion. In contrast, the noise obtained through inversion preserves critical dependencies across pixels Staniszewski et al. (2024). Thus, we employ inverted noise for video stylization, which enables partial recovery of camera and object motion; however, content not visible in the first frame remains irrecoverable, and the color is inconsistent with the reference. Therefore, we are seeking additional strategies for more accurate motion and layout preservation in the stylized video.

We further observe that the generated video content is highly sensitive to the reference, and replacing this frame with an edited or stylized one after inversion often introduces significant artifacts. We attribute this issue to the strong attention coupling between the noise and reference latents during the entire denoising stages (Figure [2](https://arxiv.org/html/2510.01686v1#S2.F2)). Specifically, the noise latent estimated through inversion tends to overfit the distribution of the original reference image. To mitigate this issue, we propose to remove the reference image during inversion, which substantially reduces these artifacts.

Figure: Figure 12: Frequency spectrum of intermediate latents across timesteps. We apply Fast Fourier Transform (FFT) along the two spatial dimensions. Lower frequencies are concentrated near the center, whereas higher frequencies are distributed toward the corners.
Refer to caption: x12.png

### A.8 Frequency Analysis of Intermediate Latents

In the frequency spectrum of the initially inverted noise, all frequency components exhibit similar intensity (Figure [12](https://arxiv.org/html/2510.01686v1#A1.F12)), characteristic of Gaussian white noise. As the denoising process progresses, the intensity of high-frequency (HF) components diminishes, while low-frequency (LF) components become more pronounced. This transition occurs most noticeably during the early stages. Therefore, our Indirect High-frequency Compensation (IHC) strategy is only applied in the early timesteps.

Figure: Figure 13: Effects on video reconstruction of LF and HF compensations in PnP inversion.
Refer to caption: x13.png

We observe that different frequency components of the intermediate latents serve distinct roles when applying PnP inversion during inference to enhance content preservation. As Figure [13](https://arxiv.org/html/2510.01686v1#A1.F13) illustrates, the low-frequency (LF) components primarily govern the appearance attributes, such as the color distribution, of the generated video. On the contrary, the high-frequency (HF) components predominantly encode layout and motion information. Consistent with characteristics observed in natural images, HF components capture edge and boundary details, which are critical for determining the structural layout. Since the HF component contains less color information, which will not affect the stylized results, we leverage it to constrain the layouts of the stylized videos in our method.

### A.9 Limitations & Future Works

Time Complexity. Similar to other training-free image & video editing approaches, FreeViS requires an inversion process to obtain the denoising trajectory, which usually doubles the inference time. In the meanwhile, since FlashAttention Dao (2024) does not provide optimized support for masked attention, the use of masked attention in Section [3](https://arxiv.org/html/2510.01686v1#S3) increases the overall time complexity. In practice, FreeViS adds approximately 30% overhead to the stylization time of the AnyV2V* implementation. A straightforward workaround is to use FreeViS for generating original–stylized video pairs prior to the supervised fine-tuning (SFT) of a video diffusion model, thereby compensating for the scarcity of such paired data. Combining with recent advances of auto-regressive video diffusion models, real-time video stylization can be possible.

Stylization Upper Bound. The stylization quality of the references is fundamentally constrained by the underlying image style transfer models. FreeViS enforces the pretrained base model to propagate style features from the reference frames across the entire video. However, the base model itself lacks the capability to explicitly parse or interpret style features. As a result, the overall quality of video stylization, both in terms of style similarity and content preservation, is fully determined by the chosen image model. As illustrated in Figures [14](https://arxiv.org/html/2510.01686v1#A1.F14) and [15](https://arxiv.org/html/2510.01686v1#A1.F15), different image-based models exhibit distinct stylization biases. Future work could explore a mixture-of-experts strategy to selectively combine their complementary strengths according to the style image or video content.

Optical Flow Estimation. FreeViS leverages optical flow to identify overlapping regions between reference frames in order to resolve temporal conflicts. However, conventional optical flow estimation methods, such as RAFT Teed & Deng (2020) used in this work, struggle with severe occlusion, leading to temporal inconsistencies in the affected regions. More advanced flow estimation and occlusion-handling techniques could be employed to mitigate this issue. Alternatively, dense and precise feature correspondences extracted with DINOv3 Siméoni et al. (2025) offer a promising direction for constructing more reliable inconsistency masks.

Figure: Figure 14: Video stylization results for FreeViS combined with different image style transfer models. ”Instant-plus” denotes InstantStyle-plus Wang et al. (2024b).
Refer to caption: x14.png

### A.10 FreeViS with Various Image Stylization Methods

Leveraging the strong prior of the diffusion model to accomplish high-quality image stylization has become mainstream in recent works Chung et al. (2024); Xu et al. (2025b); Wang et al. (2024a). FreeViS utilizes the high stylization quality of these methods to accomplish video stylization. In this section, we show that different image stylization methods have different strengths, and FreeViS can be generalized to various methods and maintain their strengths. Three state-of-the-art image style transfer models are selected for experiments: StyleID Chung et al. (2024), StyleSSP Xu et al. (2025b), and InstantStyle-plus Wang et al. (2024b).

As shown in Figures [14](https://arxiv.org/html/2510.01686v1#A1.F14) and [15](https://arxiv.org/html/2510.01686v1#A1.F15), InstantStyle-plus and StyleSSP achieve stronger high-level style alignment, whereas StyleID emphasizes low-level texture preservation. Among them, StyleSSP produces smoother object surfaces and better layout reconstruction compared to the other two methods. InstantStyle-plus provides a more favorable trade-off between visual plausibility and style preservation. Since FreeViS is compatible with all these methods, the underlying image style transfer model can be flexibly substituted to meet the requirements of different scenarios.

Figure: Figure 15: Video stylization results for FreeViS combined with different image style transfer models. ”Instant-plus” denotes InstantStyle-plus Wang et al. (2024b).
Refer to caption: x15.png

### A.11 More Results on Video Stylization

In the main paper, we omit the results of AnyV2V Ku et al. (2024) and I2VEdit Ouyang et al. (2024). For completeness, their results are provided in Figures [16](https://arxiv.org/html/2510.01686v1#A1.F16) and [17](https://arxiv.org/html/2510.01686v1#A1.F17). Both methods fail to faithfully reconstruct the essential layout of the original video and exhibit the previously discussed propagation errors, primarily due to their reliance on a single reference input. AnyV2V* (our re-implementation of AnyV2V on the Wan model) demonstrates improved layout preservation. In contrast, FreeViS effectively addresses the propagation error and achieves the highest stylization quality.

Figure: Figure 16: Qualitative comparison of FreeViS with other reference-based video editing methods. All the methods are provided with the same first reference.
Refer to caption: x16.png

Figure: Figure 17: Qualitative comparison of FreeViS with other reference-based video editing methods. All the methods are provided with the same first reference.
Refer to caption: x17.png

### A.12 More Results on Stylized T2V Generation

Additional results on stylized video generation are shown in Figure [18](https://arxiv.org/html/2510.01686v1#A1.F18). Consistent with previous observations, StyleCrafter Liu et al. (2024a) demonstrates strong style preservation but struggles with prompt fidelity and motion diversity. In contrast, StyleMaster Ye et al. (2025) achieves better text–video alignment but fails to effectively transfer the target style. The combination of FreeViS with the Wan model provides the best balance between style preservation and prompt fidelity, yielding results that are also more visually plausible.

Figure: Figure 18: Qualitative comparison of FreeViS with other stylized video generation frameworks.
Refer to caption: x18.png

### A.13 FreeViS with Various Video Diffusion Models

Figure: Figure 19: Qualitative comparison of FreeViS combined with different base models: Wan Wan et al. (2025) and HuanyuanVideo Kong et al. (2024). Their stylization results in most cases are similar, while Wan presents smoother reconstruction and better temporal consistency. The third row shows a failure case of HuanyuanVideo in consistency preservation.
Refer to caption: x19.png

To further validate the compatibility of FreeViS, we implement it on another pretrained I2V model, HunyuanVideo-I2V Kong et al. (2024). Owing to its token replacement mechanism, the first reference latent is tightly coupled with the latents of subsequent frames, which leads to severe overfitting during inversion. As a result, standard inversion fails to reconstruct the video and introduces substantial artifacts. In contrast, PnP inversion with compensation proves both more effective and essential, ensuring faithful video reconstruction. We present a qualitative comparison of FreeViS implemented on Wan Wan et al. (2025) and HunyuanVideo in Figure [19](https://arxiv.org/html/2510.01686v1#A1.F19). In most cases, the two models achieve comparable stylization performance, while the Wan model demonstrates superior robustness and temporal consistency. These results highlight the importance of leveraging the strong prior of an advanced video diffusion model to achieve higher stylization quality with FreeViS.

## Peer Reviews

confidence: 4
contribution: 2
initial_rating: 4
presentation: 3
questions: 1. Could you clarify how multiple stylized references are used in practice? 
2. How does the method handle inaccurate optical flow, especially in fast-moving scenes or occlusions? 
3. Have you tested FreeViS on more diverse styles with texture details (e.g. oil painting with strokes) or content with texture details (e.g. hair)? If so, how does it perform in terms of fidelity and consistency?
soundness: 3
strengths: 1. Originality: The idea of combining inconsistent stylized references with a pretrained I2V model in a training-free manner is practical and relevant, especially for low-resource settings.
2. Quality: The method is well-motivated and shows promising results in terms of stylization fidelity and temporal coherence. The integration of high-frequency compensation and flow cues is conceptually sound.
3. Clarity: The paper is generally easy to follow, with clear motivation and methodology. Figures and examples help illustrate the core ideas.
4. Significance: FreeViS addresses a real-world need for efficient and high-quality video stylization without the burden of training, making it potentially impactful for content creators and researchers.
summary: The paper introduces FreeViS, a training-free video stylization framework that addresses the challenges of temporal consistency and style richness in video generation. Instead of relying on paired video data or fine-tuning, FreeViS integrates multiple stylized reference images into a pretrained image-to-video (I2V) model. Its design incorporates indirect high-frequency compensation to refine content layout and motion fidelity, multi-reference integration to reduce flickering and propagation errors, and explicit optical-flow guidance to maintain style textures in low-saliency regions. The authors claim that FreeViS outperforms recent baselines in both stylization quality and coherence.
weaknesses: 1. Limited Novelty: Several components (e.g., AdaIN, cross-frame attention, flow-based guidance) have been used in prior works like Style Injection in Diffusion, StyleID, and Text-to-Video Zero. The novelty lies more in the integration to video diffusion model than in individual techniques.
2. Unclear Use of Multiple References: Although the paper claims to use multiple stylized references, the experiments and visualizations consistently show only a single style image. There is no explanation of how multiple references are selected, fused, or weighted.
3. Insufficient Evaluation of Key Components: The Indirect High-Frequency Compensation is mentioned but not thoroughly analyzed. The ablation image lacks accompanying discussion, making it hard to assess its contribution.
4. Optical Flow Reliability: The use of flow in low-saliency regions is promising, but flow errors can degrade detail preservation, especially for moving objects. Figure 7 shows only one example, and it’s unclear whether it involves motion.
5. Style Diversity: The method is tested on relatively simple styles. It’s unclear how well it generalizes to complex or abstract styles with texture details.

---

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: - Can you provide ablation studies on hyperparameters β and γ? How is the "final stage" for γ activation defined?
- Can you provide inference runtime comparisons with baselines?
- Can you justify the choice of 3 reference frames and provide ablations with different numbers/positions of references?
soundness: 3
strengths: - The IHC mechanism is well-motivated by frequency analysis showing high frequency components encode layout/motion while low frequency components control appearance.
- Solve the multi-reference challenges, dynamic injection addresses stuttering, flow-based masking resolves inconsistencies.
- The qualitative results in Figures 1, 4, 5, 6, 16, and 17 are very convincing and show a clear improvement in style propagation and temporal consistency over baselines.
- The quantitative evaluations and ablation study  are comprehensive and support the authors' claims.
- Clear improvements over baselines in both video-to-video stylization and stylized text-to-video generation tasks
summary: This paper proposes FreeViS, a training-free video stylization framework that aims to solve temporal inconsistency (flickering) in frame-by-frame methods and propagation error in single-reference I2V-based methods. The key innovation is integrating multiple stylized reference frames into a pretrained I2V model through three main technical components: 1. Indirect High-Frequency Compensation (IHC) that preserves layout/motion while maintaining stylized colors, 2. an isolated attention mechanism with dynamic injection and optical flow-based masking to handle inconsistent references, and 3. Explicit Optical Flow Guidance (EOG) to preserve textures in low-saliency regions. Experiments demonstrate improvements over existing methods in both stylization quality and temporal consistency.
weaknesses: - Missing hyperparameter ablation studies and analysis, the method combines three attention modes using hyperparameters β and γ (Eq. 8), but no ablation or analysis is provided. How sensitive is the final video quality to these parameters? Additionally, γ is only non-zero in the "final stage" but this stage is not clearly defined, what threshold determines this?
- No inference runtime comparisons with baselines are provided. This makes it difficult to assess the practical computational cost relative to quality gains.
- Reference frame selection not justified or ablated. The paper uses first, middle, and last frames, calling this an "empirically effective choice" without systematic study. Why specifically these 3 frames? How does this compare to different numbers of references? What is the performance vs. efficiency trade-off with more references?

---

confidence: 4
contribution: 2
initial_rating: 6
presentation: 3
questions: 1. Could you provide more details on how the system handles potential errors from the optical flow computation? Specifically, are there any mechanisms to correct or compensate for flow inaccuracies to prevent visible flickering in the final output?

2. Regarding Equation 2, how was the cutoff frequency for the low-pass filter determined? Could you discuss the sensitivity of the final results to this parameter and whether an ablation study was conducted to guide its selection?
soundness: 3
strengths: The main innovations are:
1. Training-Free Multi-Reference Integration: A novel pipeline that ingeniously integrates multiple stylized references into a pre-trained I2V model, effectively suppressing error propagation and eliminating flickering without any fine-tuning.

2. High-Frequency Compensation for Layout & Motion Preservation: A dedicated mechanism that utilizes high-frequency signals to constrain content structure and motion dynamics, ensuring superior temporal consistency.

3. Flow-Based Texture Preservation: The innovative use of optical flow motion cues to actively maintain and preserve style textures, enhancing the overall style richness and visual quality.
summary: This paper presents FreeViS, a training-free video stylization framework that leverages a pre-trained image-to-video model, multi-reference integration, and high-frequency compensation to generate stylized videos with rich details and strong temporal coherence.
weaknesses: 1. The proposed method's robustness is intrinsically tied to the accuracy of the optical flow algorithm. Inaccuracies in flow estimation are likely to propagate through the system, potentially leading to residual temporal flickering, which represents a fundamental limitation of the current framework.

2. A significant concern is the ambiguous contribution of the base model versus the novel components. The observed temporal coherence in the stylized videos may be predominantly attributable to the inherent strong temporal consistency of the underlying video generation model, rather than the proposed technique itself. This makes it difficult to isolate and validate the actual improvement brought by the authors' method.

---

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: - Why is the video consistency score in Table 1 lower than baselines? Why is style consistency in Table 2 weaker than StyleCrafter? A brief explanation in the paper would improve the reader’s understanding.
- How much slower or heavier is the dual-branch pipeline compared to comparison method? Please provide generation time and VRAM usage comparisons.
- How does performance change when sampling references more or less frequently? Does the choice of image stylization method for reference frames affect results? A sensitivity analysis would strengthen the claims.
- How do other metrics change across ablations, beyond the single metric currently reported? Showing full-metric ablation tables will better support the effectiveness of each component.
soundness: 3
strengths: - The paper identifies and analyzes the root causes of temporal artifacts in existing training-free stylization, and proposes a well-motivated dual-branch design to address them.
- As a training-free framework, the method is practical.
- The method naturally extends to both video-to-video stylization and stylized text-to-video generation, which increases practical value.
- Compared with existing baselines, the stylized outputs show richer texture, stronger style faithfulness, and fewer propagation errors, supported by convincing qualitative comparisons.
summary: This paper proposes FreeViS, a training-free video stylization framework that effectively addresses the propagation issues in reference-based stylization. The key idea is a dual-branch design that incorporates high-frequency compensation, additional inconsistent references, and optical-flow-guided masked attention. The method can be applied to both image-based V2V stylization and stylized T2V generation, demonstrating higher stylization fidelity and competitive temporal consistency compared to prior training-free approaches.
weaknesses: - While qualitative results are strong, the quantitative metrics in Table 1 and Table 2 are not consistently better than competing methods.
- The ablation study is limited, showing only a single metric and lacking a broader evaluation across other metrics.
- The dual-branch pipeline leads to higher memory usage and longer generation time, yet computation efficiency is only briefly mentioned in the appendix. A quantitative comparison of VRAM usage and runtime is needed.
- Several design choices, such as reference sampling frequency, number of references, and optical-flow strength, are not thoroughly analyzed. A sensitivity study would make the method's robustness and design justification clearer.


Decision: Accept (Poster)