VividFace: A Robost and High-Fidelity Video Face Swapping Framework
Authors: Hao Shao
Shulun Wang
Yang Zhou
Guanglu Song
Dailan He
Zhuofan Zong
Shuo Qin
Yu Liu
Hongsheng Li
Venue: NeurIPS 2025 poster ()
Keywords: video generation
face swapping
diffusion
Primary area: applications

Abstract: Video face swapping has seen increasing adoption in diverse applications, yet existing methods primarily trained on static images struggle to address temporal consistency and complex real-world scenarios.  To overcome these limitations, we propose the first video face swapping framework, VividFace, a robust and high-fidelity diffusion-based framework. VividFace employs a novel hybrid training strategy that leverages abundant static image data alongside temporal video sequences, enabling it to effectively model temporal coherence and identity consistency in videos.
Central to our approach is a carefully designed diffusion model integrated with a specialized VAE, capable of processing image-video hybrid data efficiently. To further enhance identity and pose disentanglement, we introduce and release the Attribute-Identity Disentanglement Triplet (AIDT) dataset, comprising a large-scale collection of triplets where each set contains three face images—two sharing the same pose and two sharing the same identity. Augmented comprehensively with occlusion scenarios, AIDT significantly boosts the robustness of VividFace against occlusions.
Moreover, we incorporate advanced 3D reconstruction techniques as conditioning inputs to address significant pose variations effectively. Extensive experiments demonstrate that VividFace achieves state-of-the-art performance in identity preservation, temporal consistency, and visual realism, surpassing existing methods while requiring fewer inference steps. Our framework notably mitigates common challenges such as temporal flickering, identity loss, and sensitivity to occlusions and pose variations.
The AIDT dataset, source code, and pre-trained weights will be released to support future research. The code and pretrained weights are available on the [project page](https://hao-shao.com/projects/vividface.html).

VividFace: A Robost and High-Fidelity Video Face Swapping Framework
Hao Shao' Shulun Wang"
Yang Zhou?
Guanglu Song?
Dailan He²
Zhuofan Zong'
Shuo Qin
Yu Liu"
Hongsheng Li
CCUHK MMLab
"SenseTime Research
*CPII under InnoHK
Abstract
Video face swapping has seen increasing adoption in diverse applications, yet existing methods primarily trained on static images struggle address temporal consistency and complex real-world scenarios. To overcome these limitations, we propose the first video face swapping framework, VividFace, a robust and high- high-fidelity diffusion- based framework. VividFace employs novel hybrid train- ing strategy that leverages abundant static image data alongside temporal video sequences, enabling it to effectively model temporal coherence and identity consis- tency in videos. Central our approach is carefully designed diffusion model integrated with specialized VAE, capable of processing video hybrid data efficiently. To further enhance identity and pose disentanglement, we intro- duce and release the Ettribute-Identii Disentanglement Triplet (AIDT) dataset, comprising a large-scale collection of triplets where each set contains three face images- two sharing the same pose and two sharing the same identity. Aug- mented comprehensively with occlusion scenarios, AIDT significantly boosts the robustness of VividFace against occlusions. Moreover, we incorporate advanced 3D reconstruction techniques as conditioning inputs to address significant pose variations effectively. Extensive experiments demonstrate that VividFace achieves state-of-the-ar performance in identity preservation, temporal consistency, and visual realism, surpassing existing methods while requiring fewer inference steps. Our framework notably mitigates common challenges such as temporal flickering, identity loss, and sensitivity to occlusions and pose variations. The AIDT dataset, source code, and pre-trained weights will be released to support future research. The code and pretrained weights are available on the project page
Introduction
In recent years, face swapping has emerged as crucial technology across various domains, from content creation (36] and privacy protection [52] to safe stunt scene production [39] and digital twin generation (35) As video is predominant medium for communication, the demand for high-quality face swapping techniques has grown substantially. Video face swapping involves extracting identity features from source face and seamlessly integrating them with the attributes (such as expressions, poses, etc.) and background of a target face while maintaining temporal consistency. However, despite the recent advancements, current face-swapping methods encounter difficulties in video contexts, as most are optimized for static images rather than dynamic video sequences.
Existing face swapping approaches can be broadly categorized into three main methodologies: 3D- based, GAN-based, and diffusion-basse methods. Traditional 3D-based methods [3] primarily utilizing 3D Morphable Models (3DMM) (5], often struggle with low-resolution outputs and face blending issues. GAN-based approaches [33 27
47 (3],
29 56)) encounter challenges with training instability, mode collapse, and producing low-resolution output, particularly in complex
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Target
Ours
Source
Source
Torget
Oun
Source
Source
Face swapping results of VividFace at 512 x 512 resolution. Our method produces high- fidelity and vivid outputs that accurately follow both pose and expression changes. Corresponding videos provided in the supplementary material.
Figure 1:
cases. Recently, diffusion models [20 have gained prominence in image synthesis tasks, offering advantages such high-fidelity output, enhanced controllability, and high training stability.
[55 and REFace 1 have highlighted the effectiveness of diffu- sion models for image-level face swapping. However, significant challenges persist when extending these methods to video face swapping, including maintaining temporal consistency, handling large pose variations, and addressing occlusions. To overcome these challenges, we propose VividFace, the first robust and high-fidelity diffusion-based video face swapping framework. VividFace introduces an innovative hybrid training strategy that integrates diverse static image data with temporal video sequences, effectively overcoming the inherent limitations of using video-only data, such insuffi- cient diversity resulting from highly similar frames within individual videos. This hybrid approach significantly expands training data diversity, enhancing the robustness and generalization capability of our model. Our framework employs a specifically designed diffusion model optimized for process- ing both static images and temporal video data. We further introduce the VidFace VAE adapted to jointly handle face images and video sequences, effectively mitigating temporal flickering typically encountered in existing video swapping methods.
Recent advancements like DiffSwap
To further enhance identity and attribute preservation, we create and release the Attribute-Identiyy Disentanglement Triplet (AIDT) dataset, consisting of million image triplets and 0.6 million video triplets. Each triplet includes a source face, target face with shared identity but different poses and expressions, and GAN- -AN-geeereted face matching the target's pose and expression but featuring distinct identity. This structured dataset significantly boosts the model's capability for disentangling identity from pose and expression. While ReliableSwap [53] also utilizes triplet-based concept, mainly addresses artifact reduction through augmentation rather than robust disentanglement. Addi- tionally, we develop a comprehensive occlusion augmentation strategy that dynamically introduces various occluding objects over target faces, significantly improving the framework's 's robustness against real-wer wol-world occlusions. To effectively manage significant pose variations, we incorporate a 3D Morphable Model (baMM)-based reconstruction as additional conditioning input. This 3D guidance ensures accurate pose and expression representation, facilitating better generalization across diverse video contexts. To further reduce information leakage and enhance robustness, retain only the pose and expression features from the reconstruction, discarding texture and identity information.
Experimental results demonstrate our framework's superiority in terms of Fréchet Video Distance (FVD), temporal consistency, and attribute/identity preservation, with fewer inference steps compared to existing methods. Besides, also demonstrate the stability and generation of our method in multiple complex cases.
To summarize, this paper makes the following contributions:
The first diffusion-based based video face swapping framework, VividFace, featuring novel vidgo-video hybrid training strategy.

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We provide and plan to release a large-scale AIDT dataset to significantly improve face feature
disentanglement.
Robustness enhancements through comprehensive occlusion augmentation and advanced 3D face reconstruction conditioning handle large pose variations effectively.
Extensive experimental analyses demonstrating superior temporal consistency, identity preservation, and visual quality, along with comprehensive ablation studies.
2 Related Work
2.1 Face Swapping
The frameworks of face swapping generally categorized into three types: 3D-based 3 47
GAN-based [33 27 2 29 56 26 38), and Iettusion-basee methods [23] 55 19]. addition to these three main approaches, FaceShifter [27 introduces a two-stage framework that generates high-fidelity swapped faces by thoroughly and adaptively exploiting and integrating the target attributes. 3D-based frameworks typically employ the parameterized 3DMM [3] model reconstruct the swapped face. Face2Face [47] transferred expressions from source to target face by fitting 3DMM face model both faces. The authors in F4 show that face swapping with robust segmentation preserves identity in intra-subject swaps and reduces recognizability in inter-subject cases. HifiFace (5T) introduced semantic facial fusion module improve photorealism. However, these 3D-hased methods yield low similarity and unrealistic textures due to limited resolution.
GAN [66 has been a powerful tool for generating realistic synthetic images. The popular algorithm DeepFakes [[I] utilizes an encoder-decoder architecture for identity-speciffc face swapping but lacks generalization. To improve adaptability, FSGAN [33 proposes subject-agnostic approach with recurrent reenactment module, inpainting and a blending module. E4S [29] reframes face swapping fine- mne-grained editing by disentangling shape and texture, using regional GAN inversion for precise feature manipulation and occlusion handling. SimSwap ( introduces an ID Injection Module and Weak Feature Matching Loss for flexible, high-fidelity identity swapping. However, GAN-based methods often struggle with balancing losses and handling shape variations or occlusions, leading to inconsistencies in illumination and identity in complex cases.
Recently, diffusion models have become leading framework for image video generation. DiffFace 23 first leverages conditional diffusion models for stable
swapping. DiffSwap 555 and FaceAdapter [19] build upon conditional inpainting paradigms to achieve high-fidelity, controllable swapping. REFace [1] improves this by reframing swapping self- sulf-supervised inpainting task. However, existing diffusion-basee methods mainly target static images, overlooking key video challenges like temporal consistency, occlusions, and large pose variations.
2.2 Diffusion Models
Diffusion models 20
58 have recently emerged as a powerful generative framework, achieving ofte-of-the-aa performance in various domains, including image synthesis [14 20 45).
editing [2 44 super-resolution 50 [5), and video generation [8 [32. Unlike GANs, which often suffer training instability, diffusion models offer more stable training process by gradually denoising data from random noise, resulting in high-fidelity outputs. Notable advancements include Stable Diffusion [40), which enhances efficiency by operating in the latent space, and SVD (6), which incorporates temporal modules to scale diffusion models for video tasks. Conditioning mechanisms like cross- attention and concatenation enhance controllability, enabling targeted generation across applications [48]. Thus, diffusion models are increasingly popular for versatile, high-quality content creation.
3 Method
3.1 Preliminaries
Our method employs Stable Diffusion (SD) 40 to-image model built on the Latent Diffusion Model (L.M)), which enables efficient image generation by operating within compressed latent space. SD uses variational autoencoder (VAE) [4] to map
as the backbone network. Stable Diffusion is text-

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Target Faces
Reference Net
Face Encoder
Training: Decoupling Faces
Source Face
Inference: Turget Faces
ViBe
Image
Motion Frames
Backbone
Diffusion
Network
Attribute
Net
Mixer
3DMM
4
3D Face
Cross Attention Feature
Iteratively denoise
Face Encoder
D
Face Region
Video
Image
Salf Attention
Cross Attention
Temporal Module
Overview of the proposed framework. During training, our framework randomly chooses static images video sequences the training data. In addition the noise 2t, three other types of inputs are integrated to guide the generation process: (1) a face region mask, which controls the generation of facial imagery; (2) 3D reconstructed face, which helps guide the pose and expression, especially in cases of large pose variations; and (3) masked source images, which supply background information. These inputs are processed through the Backbone Network, which performs the denoising operation. Within the Backbone Network, we employ cross-attention and temporal attention mechanisms. The temporal attention module ensures temporal continuity and consistency across frames. Our face encoder extracts identity and texture features from the target face, well as pose and expression details from the souree face, and uses these features produce realistic and high-fidee nigh-fidelity results.
Figure 2:
the original image To unto a latent representation Zg- reducing computational cost while preserving visual quality. The image encoded 20 E(xo) and decoded back as Tb D(2)). SD follows the Denoising Diffusion Probabilistic Model (DDPM) [20] framework, introducing Gaussian noise c the latent zg across timesteps I, generating noisy latent 2, over series steps. During inference, the model denoises z, back to mn. guided by condition features. The denoising backbone es, based on U-Net 41 trained predict the noise and remove progressively, using the objective:
B.,., [||c t,)
where represents text features derived from a CL.IP encoder 37 SD uses U-Net with cross- attention mechanisms, to fuse text embeddings with latent features, enabling fine control over generated images based on text prompts. This allows SD to generate detailed, high-fidelity images while responding effectively to user input.
3.2 Hybrid Face Swapping Framework
Video Face Swapping Task. Video face swapping aims to seamlessly transfer a source face identity onto target video while preserving the target's ''s pose, expression, lighting, and background. Although recent works like Diff'Swap [55] 23 have demonstrated promising results for static image face swapping, extending these approaches directly to videos presents substantial challenges. These include temporal distortions, flickering, occlusion sensitivity, and difficulties in managing significant pose variations.
To effectively tackle these challenges, we propose VividFace, a diffusion- video face swapping framework specifically designed for robust, temporally coherent, and high-fidelity results. VividFace introduces novel hybrid training strategy that leverages abundant image-level data alongside temporal video data. This hybrid approach enhances diversity and robustness training, significantly mitigating issues prevalent in video-only methods. Our framework initially encodes both source

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images zd E RIx3xMxW and video sequences x E Xx33xHxW into a unified latent space 20 RRXX*xHN using a specially designed VAE. Static images are treated equivalently as single- frame videos, ensuring consistent embedding. Subsequently, we train a conditional diffusion model <o(z), C) that performs latent space denoising, emphasizing temporal consistency and identity fidelity. Here, C represents the conditioning vectors, and f. indicates the denoising timestep. Due to the absence ground truth data when source and target images originate from different individuals, our model uses pairs of face images from the same identity during training. As depicted in Figure 2 training batches alternate between static image data and video sequences, ensuring efficient gradient synchronization and optimal learning dynamics.
VAA. As shown in Figure 3, our pro- posed VidFace is VAE framework designed to enhance the reconstruction quality of facial data, effectively handling both video sequences and static images. The VidFaceVAE primarily consists of (2+1)D blocks, combining 2D spatial and ID temporal convolutions to form pseudo-3D operators, For image inputs, the STFM (Spatial Temporal Fusion Module) outputs the result of the 2D ResBlock directly, bypassing the tempo- ral ResBlock. For video inputs, the STFM com- bines the outputs from both the 2D and temporal blocks using a learnable coefficient B, described 0 B x Ospatial + (1 (3) x Ovemporal, where Ospatial and Otemporal denote the output from the spatial branch and the temporal branch. We do not involve the temporal downsampling modules in our VAE framework it needs to process im- age data. The (2+1)D structure of VidFace provides two primary advantages: (1) it significantly reduces computational complexity compared to full 3D convolutions, and (2) it facilitates leveraging pretrained 2D VAE parameters and stable diffusion (SD) weights, accelerating training convergence and enhancing overall performance. In contrast to OD- DAE B), our approach does not utilize 3D causal convolutions due to their limited capacity, particularly when processing static images, and our backbone architecture does not rely on transformer-ben based designs.
[-400 Downllock
Dec0 Eoerolick
3
20 Bacillec
12:00 Netlock
Tampora Roallch
12:00
Oyptts
Parpprd
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2 Upblece
5TMM
Vadeo Only Noduk
Overview of the proposed VidFace- VAE, capable of simultaneous encoding and de- coding of both image and video data. Specific modules are designed for video inputs, which image inputs bypass when necessary.
Figure 3:
Temporal Modules. Inspired by recent approaches like EMO [88] and AnimatedDiff [8] we introduce self-attention temporal layers specifically designed for video sequences. During training with video data, we prepare additional motion frames motion enrich temporal context. The temporal attention mechanism combines these motion features with target frames at matching resolutions along the temporal dimension, improving temporal coherence. To smooth the first video clip's 's generation, motion frames are initialized as zero vectors during training.
3.3 Designs of Condition Vectors
In our framework, several carefully designed condition vectors are used to guide the generation process, ensuring accurate and consistent visual outputs for both static images and video sequences. We formulate video face swapping as a conditional inpainting task, where masked videos with cropped face regions provide the background and lighting conditions. The corresponding face regions guide the diffusion model on where are generated the faces.
In many in wild videos, faces often exhibit significant pose variations, which can lead diffusion models to produce suboptimal results, such as facial distortions and inaccurate pose estimations. To address this issue, we propose using 3D reconstruction technique to reconstruct the face and use its output as local guidance for pose and expression details. Specifically, we employ 3DMM [5] to extract BFM (Basel Face Model) coefficients, setting the texture and identity component to zero to reduce information leakage. Replacing the reconstructed face with the original target would introduce further leakage, as the ground truth face identical to the input, which could impair the model's generalization ability. To ensure that the generated face maintains the same identity as the source
5

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Video TegetFecc
cested A
Image
Trrpet
Source
carday4
Doonusling hotc
Figure 5: Visualization of our occlusion Figure 4: Visualization of our AIDT dataset, which in- data augmentation, which improves the cludes IM image triplets and 0.6M video pairs. For video facial data, we only present the target and decoupling videos.
stability and consistency of the generated
faces, as source faces can be derived from any other frame within the same clip.
face while preserving attributes (such as pose, expression, etc.., we inject cross-attention features C extracted by our face encoder global context to the diffusion model.
Face Encoder. The face encoder module in our framework plays critical role in extracting and integrating features from the target and source faces to guide the face-swapping process effectively. As illustrated in the right part of Figure [ the face encoder composed of three primary networks, each responsible for capturing distinct aspects of facial information: (1) identity net: this network focuses on extracting the core identity features from the target face; (2) texture net: this network is designed to capture detailed texture information from the target face, such as skin tone, fine facial features; (3) attribute net: the net extracts additional facial attributes from the source, such as pose, expression, and other dynamic features that contribute to a realistic and expressive representation.
The straightforward approach is to send the souree image to both the identity and texture networks, while the target image is sent to the attribute network. However, challenge arises when the source and target faces do not belong to the same person, as the ground truth unavailable in the real world. In most previous methods [1 55 23 the source and target images are assumed to be the same, meaning all three networks receive identical input. This results in difficulties for the face encoder in extracting distinct features and leading to information leakage. Specifically, this leakage causes the model to merely "copy and paste" the face region, effectively completing the task by superficially transferring facial features without meaningful feature disentangling or transformation. In contrast, our framework, built on the AIDT dataset (shown in Figure 4.. employs source images (same identity, but different attributes) and decoupling images (same attribute, but different identity). These images guide the face encoder to disentangle and fuse facial features, improving generalization across different identities during inference.
Within the Mixer module, the extracted features first undergo cross-attention operations to capture mutual dependencies. The outputs are then scaled by learned weights and fused via weighted sum. This process combines the identity, texture, and attribute features to create a comprehensive cross- attention feature representation C. This fused representation offers rich context guide the diffusion model during face generation, ensuring high fidelity and identity consistency across video frames.
3.4 Training Strategy
Our training process involves three stages to progressively enhance model performance for video face swapping. The first stage focuses on training the VidFaceVAE. where we apply reconstruction, perceptual, and KL divergence losses to ensure high- reconstruction and a well-structured latent space. The training data primarily consists of facial images and videos. Given the specifically designed architecture, the spatial modules are initialized using the original 2D VAE. In subsequent stages, the VAE is frozen and no longer updated. the second stage, we pretrain the model using image data, while the ReferenceNet and temporal modules of the backbone network remain inactive.

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Source
Target
Ours
SimSwap
REFace
FSGAN
DiffSwap
(C)
Figure 6: Qualitative comparison at 512 x 512 resolution. Our method generates high-fidelity results and handles challenging cases effectively, such as large poses (b) and occlusions (c).
The backbone is initialized from the original SD weights. Finally, perform image-video video hybrid training by activating temporal modules and introducing video data, initializing temporal modules from AnimateDiff 18 for effective temporal consistency and smooth frame transitions.
AIDT Dataset
In this section, we describe the construction of triplet pairs for our AIDT (Atrribute-Identity Disen- tanglement Triplet) dataset, which includes 1M image triplets and 0.6M video triplets, as shown in Figure The The data was generated via a pipeline of collection, detection, tracking, and post-processing, See Appendix B for details. The dataset helps the face encoder to disentangle and fuse distinct facial components- ID features, texture features from the source face, and attribute features from the decoupling face. This enhances generalization, especially when the source and target faces belong to different individuals during inference.
In addition, 5 showeases our occlusion augmentation pipeline. We collect hand and everyday object images, along with their corresponding masks, from web data sources. To simulate temporal dynamics, we design a motion trajectory animation scheme that incorporates scale, rotation, and translation over time. This augmentation strategy improves robustness under occlusion and enhances the temporal consistency synthesized videos.
Experiment
Detailed information regarding dataset preparation, network architectures, hyperparameters, and training procedures can be found in Appendix A
5.1 Evaluation Protocol
Considering that most previous baselines, such as CelebA 25 and FFHQ (2], are primarily focused on image face swapping, we propose a new benchmark for video face swapping, VidSwapBench. Our benchmark includes 200 source images and 200 high-resolution target videos, with each video containing 128 frames and single trackable face. These videos and images feature unseen identities and backgrounds, ensuring diverse and challenging dataset. To evaluate performance, we generate 200 swapped videos using our framework. For comparison. since other methods are based on image- level face swapping, we perform face swapping frame by frame for those methods. For facial data reconstruction, we use SSIM, PSNR and L.PPS (54) to evaluate the quality of reconstructed images and videos. For video face swapping, we use FVD [49 to assess the overall quality of the generated videos. The attribute transfer error is measured by pose and expression errors. We use HopeNet [2]
7

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Target
Ours
w/o occlusion aug.
Target
Ours
w/o 3D recon.
(a)
Figure 7: Ablation on the occlusion data augmentation and 3D face reconstruction.
and Deep3DFaceRecon [3] to detect these attributes, and the L2 distance to the ground truth is used as the evaluation metric. For ID retrieval, extract identity features from the source images using ArcFace, and for each swapped video, we perform face retrieval by searching for the most similar faces among all source images. The retrieval measured by the average cosine similarity of all frames, and we report the Top-1 and Top-5 accuracy.
Table 1: Comparison on VidSwapBench dataset.
Table 2: Comparison on FFHQ dataset.
Method
FVD,a4
FVD
ID@1"
ID@5t
Pose,
Expr.]
Mothod
FID
ID@1
ID@5
Posel
Expr..)
MegaF'
1280.3
72.8
6.21
MegaFS
12.0
59.6%
74.1%
3.33
1.11
HinTace 51
1377.3
386.4
74.1
82.9
6.10
0.74
HillFace
11.58
75.3%
87.1%
3,28
1.41
SimSwap
0
12428
186.6
76.5
88.5
5.12
0,76
SimSwap 9
13.8
90.6%
96.4%
2.98
1.07
FSGAN
33
1507.9
5.19
E4S 29
12.38
70.2%
82.73%
4.50
1.31
DiffFace
23
2404.7
1404.9
1.5
4.1
18.3
1.58
DifiTace 23
8.59
87.2%
94.4%
3,80
2.28
DiflSwap
53
1530.2
809
14,5
26.3
12.9
1.02
DiftSwap
55
8.58
78.2%
93.6%
2.92
1.10
REFace
REFace
Ours
12011
122.6
78.3
90.2
5.43
0.72
Ours
4.08
96.9%
99.2%
3.70
1.02
5.2 Comparisons with Existing Methods
Qualitative Results. Since videos cannot be displayed in the PDF and due submission policy restrictions on showing generated videos, we provide several comparison videos in the supplementary materials and strongly encourage the reader to view them. We perform quantitative comparison at 512 x 512 resolution. As shown in Figure 6 (a) and (d), our method generates high-fidelity swapped faces, with attributes that closely match the target faces. In Figure 6 (b), our method successfully transfers both face shape and expression under large pose variations, benefiting from the 3D reconstruction mask, while other methods exhibit generation artifacts. In Figure (c), where a toy and hand occlude the girl's face, most other methods fail to handle the occlusion properly, with the toy and hand either displaced or fused together. Additionally, many methods result in noticeable method successfully recovers the occluded areas and maintains
facial deformations. In contrast, accurate face swapping, thanks to our augmentation strategy.
Quantitative Results. In Table I we compare seven open-source methods (four GAN-based and three diffusion-based). The results show that our method outperforms others in ID retrieval and FVD, generating high-fidelity swapped face videos while preserving the source identity. It also achieves comparable performance in pose and expression, maintaining target attributes effectively. Furthermore, since our model supports both image and video face swapping, also evaluate it the standard FFHQ dataset. As shown Table 2 VividFace achieves state-of-the-art results in FID and ID retrieval, while delivering comparable performance in pose and expression preservation.
5.3 Ablation Studies
conducted comprehensive ablation experiments to analyze the contributions of different compo- nents and design choices within our framework. The quantitative results are summarized in Table 3 and qualitative visualizations are presented in Figure 7 Hybrid vs. Static Training (Exp. I): training exclusively with static images results in decreased identity preservation (Top-1 accuracy

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Table 3: Ablation training strategies and module designs
Table 4: Comparison of different VAE architectures
Exp ld
Method
FVDnl
PVD
Top-1
ID retrieval""
Top-5
Posel
Expe..
Architecture
Facial videos
Bascline
1201.3
122.6
78.3
90.2
5.45
0.772
Encoder
Decoder
SSIM
PSNR1
LPIPS]
Without SDMM Gundance
Static Traisiig
1231.9
11974
128.1
121.3
78.1
75.7
90.4
88.1
5.55
5.60
0.74
0.74
2D
2D
0.967
37.61
0.018
Merged Texture
1203.5
76.4
0.73
Inil. Texture/Attr Net CLIP
1237.9
138.2
76.0
5,30
0.72
(2-1)D
(2+1)
0,983
41.11
0.027
reduced from 78.3 to 75.7) and higher FVD scores, underscoring the critical advantage of our hybrid training approach in achieving superior temporal consistency and identity fidelity. Impact of 3D Reconstruction Conditioning (Exp. 2): removing the 3DMM conditioning slightly reduces pose and expression accuracy, highlighting the effectiveness of 3DMM guidance in handling complex pose variations. Separate Identity and Texture Networks (Exp. 3): combining identity and texture features into a single network leads reduced identity retrieval accuracy, emphasizing the necessity of separate networks for effectively disentangling and extracting distinct facial features. Initialization Strategy (Exp. 4): initializing texture and attribute networks with CLIP weights negatively affects identity preservation, demonstrating the effectiveness of our proposed initialization strategy. Impact of Occlusion Augmentation: the qualitative results in Figure 7 (a) show severe distortions when occlusion augmentation is omitted. Introducing occlusion augmentation substantially improves stability, consistency, and visual quality under occlusion scenarios. Effect of 3DMM Conditioning on Large Pose Variations: Figure 7 (b) illustrates that excluding 3D face reconstruction guidance results in significant instability and distortion for large pose variations.
VAE Architecture Analysis. Table 4 compares recon- struction performances of different VAE architectures. The baseline (SD-VAE) employs a pure 2D structure, the sec- ond model utilizes (2+1)D decoder combined with 2D encoder, and our proposed VidFace VAE employs full (2+1)D encoder-decoder. VidFace VAE outperforms alternative architectures across all metries, achieving the highest SSIM (0.983), PSNR (4I.11), and lowest LPIPS (0.027). This clearly demonstrates the advantage of effec- tively integrating spatial and temporal processing in facial video reconstruction.
Attillate weight
5.4 Face Feature Mixing Analysis
Figure 8: Ablation the different binations of texture weights and attribute weights.
We further examine the impact of varying texture and attribute weights within the face encoder. Figure shows that increasing texture weight improves identity similarity but can degrade pose and expression preservation if too high. Higher attribute weights maintain target features but reduce identity fidelity. Optimal performance is achieved by balancing these weights, set to 1.0 for identity and 0.6 for texture and attribute our experiments.
6 Conclusion
In this paper, we introduced a novel diffusion- framework for video face swapping, addressing key challenges such as temporal consistency, identity preservation, and large pose variations. Our image-video hybrid training strategy leverages both static images and video data, improving model diversity and robustness. The VidFace coupled with a custom Attribute- Iddetityy Disentangle- ment Triplet (AIDT) dataset and 3D Morphable Model integration, enables accurate face swapping while mitigating issues like flickering and occlusions. Our framework outperforms existing methods in FVD, temporal consistency, and identity preservation, with fewer inference steps. Together with the released dataset, provides a more efficient solution for high-quality video face swapping and lays the foundation for future ad advancements.

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Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non ntn-standard components.
Please refer to our LLM policy //npsr:////uuaaa cc. cc//oon/ereeccee/2255L for what should or should not be described.
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A Implementation Details
We collected approximately 550 hours of facial videos from the internet to train our models, and the facial images are partially sourced from VGGFace2-HI [00]. In our experiments, we use latent space of size 13 x 64 x 64 and a U-Net architecture for the eo denoising network. Images and video clips sampled from the dataset are resized and cropped to 512 x 512. The number of motion frames, M. is set to 4, and the generated video length, T. is set to 8 frames. For the face encoder, the identity network is based on ArcFace [2]. while the texture and attribute networks are based on DINO 17]. We use the SCRFD 177 for facial bounding box detection. The mixing coefficients of the encoder are set to 1.0 for identity features, and 0.6 for both texture and attribute features. The experiments are conducted using 16 NVIDIA A100 GPUs and optimized with AdamW [30]. In the first stage of the VAE training, the learning rate is set to 5e-6 with batch size of 32. The weights of reconstruction, perceptual, and KL divergence loss learning rate is increased to le-5, with the batch size remaining at 32. During inference, we generate video clips using the DDIM sampling algorithm for 32 steps.
1.0, 0.1, le-6 respectively. For the second and third stages, the
B AIDT Dataset details
This section describes our approach to constructing the AIDT (Atrribute-Identee Disentanglement Triplct) dataset, which consists of two parts: video clip collection and triplet pairs construction.
B.1 Video Clip Collection Pipeline
Video Collection. We collect facial videos from public platforms to capture diverse range of visual and auditory content. The video corpus includes two primary categories: static content, such as news broadcasts, interviews, and public speaking events; and dynamic content, encompassing genres like travel guides, vlogs, and musical performances. This variety ensures a comprehensive dataset, representing both controlled and spontaneous human expressions and activities across different scenarios.
Face Detection and Tracking, For each video, we first apply face detection model to identify face bounding boxes in each frame. Subsequently, all face detections are fed into a tracking procedure to generate face tracklets across frames. Each resulting tracklet is then split into video clips, each containing between 30 and 200 frames. Finally, we crop each video clip to center the head within the frame and maintain consistent aspect ratio.
Based on the preliminary clips generated in the previous stage, we further refine the clips by applying three key constraints to collect final training data. We begin by utilizing HyperIQA (46) filter out low-quality clips, setting an average quality assessment threshold score of 50 to ensure visual fidelity. Second, we enforce identity consistency within each clip by using a face recognition model to extract facial features and compute cosine similarity scores between every pair of frames, thus verifying that each clip represents single individual. Finally, we utilize an OCR model to exclude clips containing text near the facial region, minimizing visual distractions that could interfere with model training.
Data Post-Processin
B.2 Construction of triplet training dataset
For image data, we first cluster the facial images based on identity similarity. From each cluster, we randomly select two images to form target-source pair that shares the same identity but has different attributes. To generate the decoupling image, which has a different identity but the same attribute, we use the open-sourced InsightFace while preserving the gender of the original face. This approach helps to avoid the degradation in quality observed when the original and swapped faces belong to different genders. Additionally, we exclude triplets with significant facial expression discrepancies by comparing the face landmarks. For video data, the process similar, except that both the source and target images come from the same video clip, but not from the same frames as the target or motion images, which reduces the pose variation. Since video data is less abundant than image data, clustering does not yield enough pairs to form sufficient number of triplets.
create synthetic images with a distinct identity,
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Figure 9: Qualitative comparison 512 x 512 resolution. Our method generates high-fidelin results and handles challenging cases effectively, such as large poses (b) and occlusions (c). Corresponding videos provided in the supplementary material.
C More Visualization
In Figure 9 we show additional frames from the same video presented in the main text (Figure ( We further provide supplementary visual examples in Figure T0] Figure [1] Figure 12 Figure B
Figure 14, and Figure 15] The corresponding vidco results for all examples are included in the supplementary materials.
D Limitations
While VividFace demonstrates strong temporal consistency, high-fidelity visual quality, and robust- ness in challenging scenarios, it also inherits certain limitations associated with iettusion-base architectures. Specifically, compared GAN-based or lightweight encoder-decoder approaches, diffusion-bassee based models typically require more inference steps to generate each frame, resulting in slower runtime performance. This can limit the practical applicability of our method in real-time or latency-sensitee scenarios, such as live video processing or interactive applications. Although we adopt efficient design choices and optimize the number of inference steps, the trade-off between gen- eration speed and output quality remains an open challenge. Future work could explore accelerated diffusion sampling strategies or hybrid approaches that combine the strengths of both diffusion and hedd-forward architectures further improve efficiency without compromising quality.
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Source
Target
VivdFacc
Dir
RHFace
SimSwap
Figure 10: Qualitative comparison at 512 X 512 resolution. Corresponding videos are provided the supplementary material.
E
Broader Impacts
VividFace advances video face swapping with high visual fidelity and temporal consistency, offering potential positive societal impacts in several domains. These include privacy protection through identity anonymization, safer film and content production by reducing the need for risky physical stunts, the creation of digital avatars in virtual environments, and accessible tools for individuals challenges. However, the same capabilities may lead to
with communication or appearance-relee negative societal consequences if misused. High-quality face-swapping techniques can be exploited for generating deepfakes, which may contribute misinformation. identity theft, and other forms of digital deception. These risks are particularly relevant in political, social, and journalistic contexts where visual integrity crucial. To address these concerns, we plan to release our models and datasets with strict usage terms and clear documentation. We encourage responsible use and support the development of detection tools and watermarking techniques to distinguish generated content from real footage. Furthermore, we advocate for continued dialogue in the research community around ethical deployment, regulatory considerations, and public education to minimize harm while enabling beneficial use cases. As with any powerful generative technology, the societal impact of VividFace will depend not only on tool itself, but on how governed and integrated into real-world systems.
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Soence
Target
VividFace
DiftSwap
ESGAN
REPace
SimSwap
SIMGSWAP
Figure 11: Qualitative comparison at 512 x 512 resolution. Corresponding videos are provided in the
supplementary material.
Source
Target
VividFace
DiffS
ESGAN
REFace
SimSwap
Figure 12: Qualitative comparison at 512 512 resolution. Corresponding videos are provided the supplementary material.
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Sourco
ViridFace
S
wap
ESGAN
REFace
SimSwap
Figure 13: Qualitative comparison at 512 x 512 resolution. Corresponding videos are provided in the
supplementary material.
Source
Turget
VividFace
10azin
nnazin
HADANET
DinSwap
ESGAN
REPace
SimSSap
Figure 14: Qualitative comparison 512 512 resolution. Corresponding videos are provided in the supplementary material.
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Target
VividFace
SMASWAD
Figure 15: Qualitative comparison 512 512 resolution. Corresponding videos are provided in the supplementary material.
26


Reviews:
[{"review_id": "16ApEZIZnW", "reviewer": "Reviewer_yp2A", "summary": "This paper focuses on robust video face swapping by adapting diffusion model with image-video hybrid training strategy. To improve the robustness of proposed method, a large-scale AIDT dataset is constructed and comprehensive occlusion augmentation is adopted. The writing is good in general. However, the overall framework, training strategy and other parts of Sec 3 may need proofreading for better readability. The experimental results show its effectiveness of proposed method.", "questions": "（1）Although robustness enhancement is adopted, why the performance in pose and expression only comparable to SOTA methods?\n  (2)  Is the encoder-decoder is firstly trained on video data and secondly training on image data? Then random selection of previous video and image data is conducted for hybrid training?", "limitations": "(1) The performance improvement between baseline and static training is limited, which weakens the novelty of proposed method.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengthens:\n(1)It is the first diffusion-based video face swapping framework;\n(2)It constructs a large-scale AIDT dataset to significantly improve face feature identity-expression disentanglement;\n\nWeaknesses:\n(1)Diffusion-based method limits the practical applicability;\n(2)It is better to separate the training and inference stages of Fig 2;\n(3)For face encoder (Line 200-207), the 'source' and 'target' are different from the corresponding part of Fig 2.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your helpful comments on the paper. We have addressed them as the following:\n\n**Q1: Diffusion-based method limits the practical applicability**\n\nWe acknowledge that diffusion models are slower during inference, which can limit real-time deployment. However, recent advancements—including latent-space sampling, optimized U-Net architectures, and fast sampling techniques (e.g., DDIM)—have significantly improved inference speed. Moreover, diffusion-based methods have already been adopted in industrial-grade tools such as PiKa and Runway, indicating their increasing practical viability.\n\nIn this work, our primary focus is to advance generation quality and robustness in challenging video face swapping scenarios, such as those involving occlusions or large pose variations. Efficiency-oriented improvements, such as model acceleration and architectural streamlining, are promising future directions. We are also actively exploring the integration of DiT-based backbones to further enhance speed without compromising quality.\n\n\n**Q2: It is better to separate the training and inference stages in Fig. 2**\n\nThank you for pointing this out. We agree that separating the training and inference stages would improve clarity. While we have created a figure to explicitly illustrate the inference pipeline, we regret that we are unable to include additional images in the rebuttal due to policy constraints. We have revised Figure 2 accordingly in the manuscript.\n\n**Q3: Source and target in Lines 200–207 are inconsistent with Fig. 2**\n\nWe apologize for the confusion. This was due to an error in the ordering of ‘source’ and ‘target’ terms. Thank you for catching this—both the manuscript and figure have been updated to ensure consistency.\n\n\n**Q4: Although robustness enhancement is adopted, why the performance in pose and expression only comparable to SOTA methods?**\n\nThank you for the valuable question. As you rightly pointed out, **most widely-used benchmarks such as FFHQ (reported in Table 2) and VidSwapBench  (reported in Table 1) contain relatively few instances of extreme poses or severe occlusions**. While these challenging cases are rare in these benchmarks, they are often the most disruptive in real-world applications. Our robustness strategies are explicitly designed to handle these difficult scenarios.\n\nAlthough this focus may lead to only modest gains on standard quantitative benchmarks, we would like to emphasize that the improvements are much more apparent in qualitative results—particularly under extreme conditions, as illustrated in Fig. 6 and 7. We believe these results highlight the practical value of our approach beyond standard metrics, and we truly appreciate the reviewers' attention to this aspect.\n\n\n**Q5: Is the encoder-decoder is firstly trained on video data and secondly training on image data? Then random selection of previous video and image data is conducted for hybrid training?**\n\nOur training for the VAE follows a unified single-stage strategy, where image and video data are randomly interleaved across batches. To ensure stable gradient synchronization, each batch contains only a single modality—either images or videos.\nThe spatial components of VidFaceVAE are initialized from a pretrained 2D SD-VAE, and the model is then jointly trained on both modalities. This enables effective spatial-temporal representation learning while preserving diversity in appearance and motion. We believe this offers a clean and efficient training paradigm, and we sincerely appreciate the reviewers for prompting this clarification. We have revised the manuscript to better reflect this design.\n\n**Q6: Performance gap between baseline and hybrid training is small**\n\nWhile the performance improvement may appear modest in aggregate metrics, our method demonstrates consistent gains across FVD, ID retrieval, pose, and expression accuracy (Table 3, Exp. 1), indicating stable and reliable improvements across diverse aspects.\nNotably, models trained solely on static images tend to suffer from inferior temporal consistency and more noticeable visual artifacts in video generation. To further support this, we evaluate our method on VBench[1]—a comprehensive and challenging video benchmark—and observe clear improvements in consistency, flicker reduction,  and motion smoothness compared to both static training and existing state-of-the-art methods.\n\n\n| Models             | Subject Consistency | Background Consistency | Temporal Flickering | Motion Smoothness | Aesthetic Quality | Imaging Quality | Temporal Style |\n|--------------------|---------------------|------------------------|----------------------|-------------------|-------------------|-----------------|----------------|\n| DiffSwap           | 96.10%              | 93.44%                 | 96.76%              | 98.22%            | 42.97%            | 56.94%          | 8.42%         |\n| FSGAN              | 97.07%              | 94.87%                 | 98.15%              | 98.67%            | 47.22%            | 60.23%          | 10.25%         |\n| Reface             | 96.87%              | **96.51%**                 | 98.18%              | 98.72%            | 47.84%            | 61.70%          | 10.40%         |\n| SimSwap            | 97.39%              | 96.50%                 | 98.10%              | 98.89%            | 43.07%            | 62.05%          | 10.78%         |\n| ViVidFace   (static training)       | 97.73%              | 96.19%                 | 98.06%             | 98.91%            | 48.12%            | 64.19%        | 10.80%      |\n| ViVidFace          | **97.93%**              | 96.39%                 | **98.74%**              | **99.18%**            | **49.56%**            | **64.65%**          | **11.90%**         |\n\n[1]: VBench: Comprehensive Benchmark Suite for Video Generative Models"}, {"review_id": "mLumngWrBH", "reviewer": "Reviewer_FAkC", "summary": "This paper introduces VividFace, a diffusion-based framework for high-fidelity video face swapping. The authors identify key challenges in existing methods, namely the lack of temporal consistency, poor identity preservation, and vulnerability to real-world complexities like occlusions and large pose variations. To address these, VividFace proposes several techniques. The paper demonstrates through extensive experiments that VividFace achieves state-of-the-art performance on both video and image face swapping tasks, outperforming existing GAN-based and diffusion-based methods in terms of visual quality, identity preservation, and temporal consistency.", "questions": "Please refer to the weaknesses part.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\n\n1. This paper tackles a very relevant and challenging problem in generative AI: high-fidelity video face swapping. The release of the code, models, and the novel AIDT dataset is a major contribution that will undoubtedly facilitate future research in this area.\n\n2. The technical quality of the work is excellent. The proposed framework is well-designed, and each component's inclusion is well-motivated. The experimental evaluation is comprehensive and rigorous. The authors not only compare against a strong set of recent baselines on a new, challenging benchmark (VidSwapBench) but also conduct a thorough set of ablation studies that convincingly validate their design choices. The qualitative results shown in the paper are very impressive.\n\nWeaknesses:\n1. While the paper's focus is on generation quality, a practical limitation of diffusion models is their inference speed. The paper acknowledges this in the limitations section but does not provide a quantitative comparison of inference time against the baselines in the main experimental tables. For a video task, metrics like frames-per-second are highly relevant and would provide a more complete picture of the trade-offs involved.\n\n2. I notice that the eyes of the generated face are not the same as the target face, and the eye contact is not as vivid as human beings. This is a challenging problem. But humans are very sensitive to this.", "was_revised": true, "final_justification": "Thank you to the authors for their rebuttal, which addressed my concerns. After reviewing the rebuttal and the comments from other reviewers, I have decided to maintain my original score for this paper", "rebuttal": "Thank you for your helpful comments on the paper. We have addressed them as the following:\n\n**Q1: While the paper's focus is on generation quality, a practical limitation of diffusion models is their inference speed. The paper acknowledges this in the limitations section but does not provide a quantitative comparison of inference time against the baselines in the main experimental tables. For a video task, metrics like frames-per-second are highly relevant and would provide a more complete picture of the trade-offs involved.**\n\nThank you for raising this important point. While our work emphasizes generation quality and robustness, we agree that inference efficiency is a crucial factor for practical deployment. Our current framework achieves approximately 2 frames per second (FPS) on an NVIDIA A100 GPU at 512×512 resolution, which ensures high fidelity and strong temporal consistency. In comparison: DiffSwap (CVPR 2023) reports ~0.028 FPS,\nDiffFace achieves ~0.68 FPS (3× slower than ours), REFace (WACV 2025 Oral) runs at ~1 FPS (2× slower).\n\nWhile our speed is indeed lower than GAN-based methods such as SimSwap (resolution: 256²) or FSGAN (resolution: 128²), which trade quality for speed, our method significantly outpaces other diffusion-based frameworks. We will include these FPS comparisons in the revised version to provide a clearer picture of the quality-efficiency trade-off.\n\n\n| Method    | Venue         | Modality | Resolution | FPS      | Relative Speed |\n|-----------|---------------|----------|------------|----------|----------------|\n|Diffusion-Based|------------------|----------|------------|----------|------------------|\n| Ours      | NeurIPS 2025  | Video    | 512×512    | ~2.0     | 1× (baseline)  |\n| DiffSwap  | CVPR 2023     | Image    | 512×512    | ~0.028   | ~71× slower    |\n| DiffFace  | arXiv 2022    | Image    | 512×512    | ~0.68    | ~3× slower     |\n| REFace    | WACV 2025     | Image    | 512×512    | ~1.0     | ~2× slower     |\n|GAN-Based|------------------|----------|------------|----------|------------------|\n| SimSwap   | ACM MM  2020  | Image    | 256×256    | ~12.8    | ~6× faster     |\n| FSGAN     | ICCV 2019     | Image    | 256×256    | ~4.6     | ~2× faster     |\n\n\n\n**Q2: I notice that the eyes of the generated face are not the same as the target face, and the eye contact is not as vivid as human beings. This is a challenging problem. But humans are very sensitive to this.**\n\n\nWe appreciate this insightful observation. Indeed, humans are highly sensitive to gaze direction and eye contact fidelity. In the face swapping formulation, eye identity (e.g., shape, iris) is preserved from the source face, while gaze direction and expression are inherited from the target video through the attribute encoder. We find that eye identity is generally maintained reasonably well from the source in the VividFace.\n\nAlthough our attribute network captures high-level facial movements—including pose and coarse eye dynamics—it does not explicitly enforce fine-grained gaze alignment or vividness. This can lead to subtle deviations in pupil orientation or liveliness. Improving eye realism is a meaningful and challenging direction. In future work, we plan to explore:\n\n1. Explicit gaze estimation and alignment losses to better match pupil direction,\n\n2. Eye-specific perceptual or adversarial losses focused on iris and specular highlights,\n\n3. Localized eye-region enhancement modules, including cropping-based super-resolution."}, {"review_id": "ikY7NOanhu", "reviewer": "Reviewer_yWwo", "summary": "This paper introduces a diffusion-based video face-swapping framework that addresses temporal consistency and identity preservation. They design a VidFaceVAE, which consists of (2+1)D blocks and is trained on both static images and videos to improve temporal coherence. The authors also propose AIDT, a new dataset designed to disentangle identity and pose, which enhances robustness to occlusions. With additional 3D conditioning for pose variation, VividFace achieves state-of-the-art results in identity retention and visual quality, with fewer inference steps.", "questions": "Questions:\n1. Can you generate videos with arbitrary length while maintaining temporal consistency?\n2. What is the inference speed of this method?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strength:\n1. Propose to train their video face swapping network with both image and video data. And propose a new dataset, AIDT.\n2. Introducing 3DMM into the face swapping process, which guarantees the attributes (eg, pose, expression) to be unchanged.\n3. Propose AIDT, a new dataset designed to disentangle identity and pose, which enhances their method’s robustness to occlusions.\n\nWeakness: \n1. The authors claim they are the first video face-swapping network. However, several video face-swapping methods have been proposed recently, e.g., DynamicFace and HiFiVFS.\n2. Unsatisfying results. The generated videos still contain several artifacts, \\eg, large opening mouth, hair on the forehead.\n3. The comparison cases are not sufficient. Most of the test cases are man-man or woman-woman swapping. Including more cases under different genders might be better to show the superiority of the proposed method.\n4. Although 3DMM can effectively preserve the motions of the target video, it suffers from incorrect estimations under large headposes. Also, estimation 3dMM is quite slow.", "was_revised": true, "final_justification": "The authors’ response addresses some of my concerns, and I have decided to raise my score accordingly. Additionally, recently, there have been several existing works on face swapping, such as [1], [2], [3]. It would strengthen the paper if the authors could include a discussion of these related studies.\n\n[1] CanonSwap: High-Fidelity and Consistent Video Face Swapping via Canonical Space Modulation\n\n[2] Controllable and Expressive One-Shot Video Head Swapping\n\n[3] DreamID: High-Fidelity and Fast diffusion-based Face Swapping via Triplet ID Group Learning", "rebuttal": "Thank you for your helpful comments on the paper. We have addressed them as the following:\n\n\n**Q1: The authors claim they are the first video face-swapping network. However, several video face-swapping methods have been proposed recently, e.g., DynamicFace and HiFiVFS.**\n\nThank you for pointing out the concurrent works *DynamicFace* and *HiFiVFS*. At the time of our submission, both were available only as arXiv preprints, without official publication or publicly released code. We acknowledge that our original claim could have been clearer, and we will revise the manuscript to explicitly state that our work is concurrent with these recent efforts. We will also include appropriate references and discussion of these methods in the related works section.\n\n**Q2: Unsatisfying results. The generated videos still contain several artifacts, \\eg, large opening mouth, hair on the forehead.**\n\nWe appreciate your observation regarding certain artifacts, such as exaggerated mouth openings or imperfections around the hair region. These are indeed challenging areas in video face synthesis. While we acknowledge the presence of such issues in some cases, our results demonstrate state-of-the-art performance compared to existing video face-swapping methods—particularly under difficult conditions involving occlusions, large pose variations, or complex motion.\n\nOne contributing factor to these artifacts is the imperfect disentanglement between identity and attribute features. In certain instances, entanglement can cause the model to misrepresent fine-grained attributes (e.g., mouth dynamics or hair texture) while trying to preserve identity, leading to visual inconsistencies.\n\n**Q3: The comparison cases are not sufficient. Most of the test cases are man-man or woman-woman swapping. Including more cases under different genders might be better to show the superiority of the proposed method.**\n\nWe appreciate your concern regarding the diversity of comparison cases. To clarify, our evaluation set (VidSwapBench) already includes diverse identity combinations, and the current cases were not intentionally biased toward similar-looking pairs. \n\nMoreover, we do include several cross-gender examples, such as the upper-right example in Figure 1, and Figures 6(b) and 6(c). These examples illustrate our model's superior generalization across genders compared to other methods.\n\nHowever, we agree that cross-gender face swapping is inherently more challenging. Currently, our dataset (AIDT) consists predominantly of same-gender pairs to facilitate stable training. Based on your suggestion, we will include additional cross-gender face swapping examples in the supplementary materials to provide a clearer picture of our method's strengths and limitations in more diverse scenarios.\n\nBased on your valuable feedback, we have conducted additional experiments specifically targeting cross-gender scenarios. In particular, we curated two new test sets:\n\n- Table 1: Woman → Man (source image is female, target video is male)\n- Table 2: Man → Woman (source image is male, target video is female)\n\nEach set contains 1,000 distinct image–video pairs.\n\n\n| Cross-Gender Swap (Woman → Man) |\n| Method        | FVD32↓  | FVD128↓ | ID@1↑ | ID@5↑ | Pose↓ | Expr.↓ |\n|---------------|---------|---------|-------|-------|-------|--------|\n| MegaFS        | 1334.7  | 219.1   | 69.1  | 79.9  | 6.53  | 0.78   |\n| HifiFace      | 1405.3  | 407.6   | 71.7  | 81.6  | 6.29  | 0.75   |\n| SimSwap       | 1279.5  | 204.6   | 73.4  | 86.9  | **5.43** | 0.82   |\n| FSGAN         | 1580.1  | 463.9   | 22.3  | 37.5  | 5.47  | 0.80   |\n| DiffFace      | 2487.8  | 1478.6  | 1.3   | 3.1   | 19.2  | 1.66   |\n| DiffSwap      | 1593.2  | 888.4   | 13.0  | 24.1  | 13.4  | 1.16   |\n| REFace        | 1376.8  | 328.7   | 69.1  | 83.0  | 6.78  | 0.95   |\n| **Ours**      | **1239.2** | **126.1** | **76.5** | **88.3** | 5.61  | **0.73** |\n\n\n\n| Cross-Gender Swap (Man → Woman) |\n| Method        | FVD32↓  | FVD128↓ | ID@1↑ | ID@5↑ | Pose↓ | Expr.↓ |\n|---------------|---------|---------|-------|-------|-------|--------|\n| MegaFS        | 1307.4  | 209.9   | 69.6  | 80.3  | 6.48  | 0.78   |\n| HifiFace      | 1418.2  | 398.7   | 71.3  | 81.8  | 6.33  | 0.73   |\n| SimSwap       | 1264.9  | 191.2   | 73.7  | 86.7  | **5.29**  | 0.81   |\n| FSGAN         | 1531.7  | 438.8   | 21.8  | 37.9  | 5.31  | 0.78   |\n| DiffFace      | 2453.6  | 1442.3  | 1.1   | 3.7   | 18.9  | 1.62   |\n| DiffSwap      | 1570.3  | 839.6   | 12.8  | 25.1  | 13.1  | 1.13   |\n| REFace        | 1357.8  | 323.5   | 68.7  | 84.5  | 6.81  | 0.95   |\n| **Ours**      | **1256.6** | **127.3** | **76.6** | **88.9** | 5.55  | **0.72** |\n\n\n\n**Q4: Although 3DMM can effectively preserve the motions of the target video, it suffers from incorrect estimations under large headposes. Also, estimation 3dMM is quite slow.**\n\n1. **Efficiency.**\nWe acknowledge your concern about the reliability and efficiency of 3DMM estimation, particularly under extreme head poses. To clarify, we utilize the learning-based 3D reconstruction method **Deep3DFace**'s (official repo)[1], which is notably fast and efficient compared to traditional optimization-based approaches. Specifically, this approach achieves approximately **8ms** (from input image, model forward, rednering) per image on our NVIDIA A100 SXM4 (80GB) GPU at batch size = 1. With larger batch sizes (e.g., bs=32), processing speed further improves to **<1ms** per image, making it practical for video applications.\n\n2. **Reliability at large poses.**\nEmpirically, learning-based 3D reconstruction is more stable than 2D-only cues under large yaw/pitch because it predicts a full 3D face with semantic correspondences, enabling consistent PnP pose solving and disambiguation of depth/scale across frames. Public results from representative methods—Deep3DFaceRecon[1], 3DDFA-V2[2], and SynergyNet[3]—specifically target stability and accuracy under full-pose conditions; 3DDFA-V2 (ECCV’20) and SynergyNet (3DV’21) report strong accuracy and real-time speeds in unconstrained, large-pose settings.\nNevertheless, we acknowledge that even advanced learning-based 3DMM methods can still struggle with extreme poses. We plan to explore more robust 3D reconstruction approaches or complementary pose-estimation methods to improve accuracy further in future work.\n\n[1]: Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set, https://github.com/sicxu/Deep3DFaceRecon_pytorch\n\n[2]: Towards Fast, Accurate and Stable 3D Dense Face Alignment.\n\n[3]: Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry\n\n\n\n**Q5: Can you generate videos with arbitrary length while maintaining temporal consistency?**\n\nYes, our method supports generating videos of arbitrary length while maintaining good temporal consistency. To achieve this, we sequentially generate clips, utilizing the last three frames of the previously generated clip as motion context for the next clip. In practice, we have verified consistent quality in sequences of approximately one minute (about 1,000 frames). For significantly longer videos, a slight degradation might occur due to accumulated error, but restarting the process periodically mitigates this issue.\n\n\n\n**Q6: What is the inference speed of this method?**\n\nOur pipeline achieves approximately 2 frames per second (FPS) on an NVIDIA A100 GPU at a resolution of 512×512, providing high fidelity and temporal coherence. Although this is slower than GAN-based approaches (e.g., SimSwap at 256² or FSGAN at 256²), our diffusion-based framework is significantly faster than comparable diffusion-based methods such as:\n\n| Method    | Venue         | Modality | Resolution | FPS      | Relative Speed |\n|-----------|---------------|----------|------------|----------|----------------|\n|Diffusion-Based|---------------|----------|------------|----------|----------------|\n| Ours      | NeurIPS 2025  | Video    | 512×512    | ~2.0     | 1× (baseline)  |\n| DiffSwap  | CVPR 2023     | Image    | 512×512    | ~0.028   | ~71× slower    |\n| DiffFace  | arXiv 2022    | Image    | 512×512    | ~0.68    | ~3× slower     |\n| REFace    | WACV 2025     | Image    | 512×512    | ~1.0     | ~2× slower     |\n|GAN-Based|---------------|----------|------------|----------|----------------|\n| SimSwap   | TPAMI  2024  | Image    | 256×256    | ~12.8    | ~6× faster     |\n| FSGAN     | ICCV 2019     | Image    | 256×256    | ~4.6     | ~2× faster     |"}, {"review_id": "K7DoM7pe0P", "reviewer": "Reviewer_QBbm", "summary": "This paper introduces VividFace, a diffusion-based framework for high-fidelity video face swapping, leveraging both static images and video sequences for training. Key contributions include:\n1. A triplet dataset (same ID/different pose, same pose/different ID) to improve identity-pose disentanglement.\n2. An augmentation strategy for occlusions, and 3D reconstruction encoding for conditional input.\n3. Comprehensive evaluations on temporal smoothness, occlusion robustness, pose variation, and identity preservation.", "questions": "Have you experimented with simplifying the conditioning pipeline (e.g., reducing motion frame encodings)? Does redundancy improve results, or is it an architectural constraint?\n\nIs the identity preservation issue in Figure 6/Group 1–6 due to dataset bias (e.g., AIDT lacks diverse pairs) or a fundamental trade-off in the framework?\n\nCould you include comparisons on the same source/target pairs as SimSwap or other methods to ensure fair evaluation?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\n- The proposed framework are intuitive and straghtforward.\n- The results are natural-looking with improved temporal smoothness over prior work. The proposed method handles occlusions better than baseline methods.\n\n\nWeaknesses\n- The conditioning input is redundant. The motion frame is encoded three times, and the masked source image overlaps with the face region input. This may introduce inefficiency without clear justification.\n- Figure 2 is confusing. The blue block is ambiguously labeled (sometimes the temporal module, sometimes latent representation z_0). Missing tensor dimensions for latent representations (blue, yellow, gray blocks), making it harder to reproduce.\n- The methods works well when source/target faces are similar (e.g., Figure 1, all people have high checkbones) but struggles with large identity gaps (e.g., Figure 6). Results in Groups 1–6 either: Use overly similar identities, or Fail to preserve identity when faces differ significantly.\n- Fair Comparison Concerns: Comparisons with SimSwap/others use different examples. Testing on identical pairs would strengthen validity.", "was_revised": true, "final_justification": "After careful consideration, I recommend acceptance of this paper as it makes contributions to the field of video face swapping applications.\n\nWhile video face swapping represents a specialized domain within computer vision, it can be viewed as an important fine-grained task in video generation that offers precise semantic control. The proposed framework and rigorous evaluation presented in this work advance the state-of-the-art while providing valuable insights that could inform broader video generation research.", "rebuttal": "Thank you for your helpful comments on the paper. We have addressed them as the following:\n\n**Q1: The conditioning input is redundant. The motion frame is encoded three times, and the masked source image overlaps with the face region input. This may introduce inefficiency without clear justification.**\n\nThank you for pointing this out. We would like to clarify the computational overhead introduced by our conditioning inputs:\n\n1. **Face Region Input:**\n\n\nThe face region input introduces minimal computational overhead. Specifically, it only increases the number of input channels in the first convolutional layer from 12 to 13, adding an extra computational cost of approximately $\\(2.5 \\times 10^{-6}\\)$. Therefore, subsequent model complexity and inference speed remain virtually unaffected.\n\n2. **Motion**\n\nThank you very much for this helpful comment regarding redundancy in motion-frame encoding. To clarify, each motion frame is encoded only once, prior to the iterative denoising process. Given that our pipeline typically involves around 30 denoising steps, the extra computational overhead introduced by encoding motion frames is approximately 3%, which we consider negligible in practice.\nFor clarity, we provide the full implementation details in the supplementary materials (infer.py, lines 480–523). Additionally, we provide simplified pseudocode below for your convenience:\n\n```python\n# Encode motion frames once per inference\nmotion_features = encode_motion(motion_frames)\n\n# Iteratively denoise latent representation (~30 steps)\nfor step in range(denoising_steps):\n    # Update latent using motion features and additional conditioning inputs\n    latents = denoise(latents, motion_features, other_conditions)\n\n```\n\nHowever, we fully acknowledge your valuable suggestion. In future iterations, we will explore further simplifying the conditioning inputs to assess whether the current redundancy indeed contributes positively to model performance or represents a constraint of our current architecture.\n\n\n**Q2: Figure 2 is confusing. The blue block is ambiguously labeled (sometimes the temporal module, sometimes latent representation z0). Missing tensor dimensions for latent representations (blue, yellow, gray blocks), making it harder to reproduce.**\n\n\n1. We selected similar shades of blue for different concepts (temporal modules <light blue> and latent representation <dark blue>). We will revise Figure 2 using clearly distinct colors to improve readability.\n\n2. Regarding missing tensor dimensions, we have included complete, runnable code in the supplementary material for full reproducibility. However, we recognize explicit tensor dimensions in figures facilitate understanding, and we will add these explicitly in the final revision.\n\nFor reference, the tensor shapes are:\n\nYellow: $B \\times T \\times 3 \\times H \\times W$\n\nPink: $B \\times T \\times 3 \\times H \\times W$\n\nGray: $B \\times T \\times 3 \\times H \\times W$\n\nLight Blue: $B \\times T \\times 1 \\times H \\times W$\n\nThank you for helping us improve the clarity of our method's presentation.\n\n**Q3: The methods works well when source/target faces are similar (e.g., Figure 1, all people have high checkbones) but struggles with large identity gaps (e.g., Figure 6). Results in Groups 1–6 either: Use overly similar identities, or Fail to preserve identity when faces differ significantly.**\n\nYou raise an insightful observation regarding performance gaps with large identity differences:\n\n- We conducted an extra quantitative experiment analyzing identity preservation under varying degrees of identity similarity. We confirm that our approach, like other face-swapping frameworks, performs best when the identities involved share similar facial structures and appearance attributes.\n\n- To explicitly quantify this, we compared our method against other baselines across a variety of identity similarity levels. We observed that, although performance naturally degrades with increased identity differences, our method still consistently achieves state-of-the-art identity preservation compared to alternatives.\n\n- Unfortunately, due to the rebuttal policy, we cannot upload additional visual comparisons at this stage. We will include these analyses and visualizations comprehensively in our supplementary materials upon revision.\n\nWe acknowledge that swapping faces across significantly different identities (e.g., cross-gender or large age gaps) remains challenging and a critical area for future research. We are actively working to address these challenges further.\n\n\n\n**Q4: Fair Comparison Concerns: Comparisons with SimSwap/others use different examples. Testing on identical pairs would strengthen validity.**\n\nWe appreciate this important concern regarding the fairness of our comparisons:\n\n- To clarify explicitly, all quantitative and qualitative evaluations in our submission—including Figures 6, 10–15, and Tables 1 and 2—are performed using identical source-target pairs across all methods, ensuring a fair and direct comparison of model performance.\n\n- If your concern relates specifically to evaluating methods on source and target faces from the same identity (self-swapping), we note that such self-face swaps generally constitute trivial tasks with near-perfect results across all methods. \n\n\n**Q5: Have you experimented with simplifying the conditioning pipeline (e.g., reducing motion frame encodings)? Does redundancy improve results, or is it an architectural constraint?**\n\n\nYes, we conducted ablation studies to explore the impact of simplifying the conditioning pipeline, including reductions in motion frame encodings and face-region inputs. Our findings indicate that:\n\n- **Motion encoding** contributes positively to temporal consistency. While its removal does not drastically degrade performance, we observed a modest drop in temporal metrics (e.g., FVD32, FVD128), suggesting that this redundancy provides valuable contextual cues rather than adding unnecessary complexity.\n\n- **Face-region conditioning** also plays a crucial role in maintaining visual realism. Excluding it led to slight decreases in identity preservation and expression accuracy, underscoring its importance in high-fidelity generation.\nThese results imply that the current level of conditioning is beneficial and not merely an artifact of architectural choices. We will include comprehensive ablation details and discussions in the supplementary materials.\n\n| Method        | FVD32↓ | FVD128↓ | ID@1↑ | ID@5↑ | Pose↓  | Expr.↓ |\n|---------------|---------|----------|-------|-------|--------|--------|\n| Baseline      | **1201.1** | **122.6** | **78.3** | **90.2** | **5.43** | **0.72** |\n| Remove Motion      | 1222.7 | 125.8 | 78.1 | 88.8 | 5.56 | 0.75 |\n| Remove Face Region      | 1205.1 | 123.1 | 77.9 | 89.7 | 5.48 | 0.73 |\n\n\n**Q6: Is the identity preservation issue in Figure 6/Group 1–6 due to dataset bias (e.g., AIDT lacks diverse pairs) or a fundamental trade-off in the framework?**\n\nThe core challenge arises from the nature of face swapping, which inherently lacks ground-truth supervision when swapping between different identities. High-quality face swapping across substantially dissimilar identities (e.g., significant differences in facial geometry, or across gender/age) remains universally challenging. While our AIDT dataset offers considerable diversity, it primarily features same-gender identity pairs to stabilize training and ensure data quality.\n\nWhen swapping between highly dissimilar faces (e.g., square to round face, male to female), the swap may technically succeed, but the result can still appear perceptually unrealistic or unconvincing to human observers. This reflects a broader challenge in the field: visual plausibility may degrade even when the system technically preserves identity features.\n\nWe acknowledge that this limitation stems both from the training data’s constrained diversity and from an inherent trade-off common to most existing frameworks. Nevertheless, our evaluations clearly demonstrate that our method achieves state-of-the-art identity preservation, though we agree that further improvement is necessary and ongoing.\n\n**Q7: Could you include comparisons on the same source/target pairs as SimSwap or other methods to ensure fair evaluation?**\n\nAs explained above, all evaluations in our manuscript consistently use identical source-target pairs across all methods (including SimSwap) to ensure a fair comparison. If your concern is specifically related to self-swapping cases (i.e., swapping within the same identity), we would like to clarify that such evaluations often produce trivial outcomes with near-perfect accuracy and therefore offer limited comparative insight."}]

Author rebuttal:

Meta-review: All four reviewers rated the paper positively, with concerns addressed convincingly in the rebuttal. VividFace presents a diffusion-based video face-swapping framework, achieving SOTA in temporal consistency and identity preservation through novel contributions. Key concerns like inference speed, cross-gender robustness, conditioning efficiency were resolved in rebuttal. Two minor limitation persist: (1) some of the generated videos containing noticeable artifacts, e.g., large open mouths and hair on the forehead; and (2) the 3DMM’s incorrect estimations under large head poses. Overall its merits outweigh minor limitations. The authors need to resolve all the issues raised by reviewers and reflect in the final version.
Decision: Accept (poster)