# CanonSwap: High-Fidelity and Consistent Video Face Swapping via Canonical Space Modulation

arXiv: https://arxiv.org/abs/2507.02691 · HTML: https://mlsanity.com/paper/2507.02691/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 60 references that are in the map.
- First posted on arXiv 3 Jul 2025.

## Builds on (most cited first, 30 of 41)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [First Order Motion Model for Image Animation](https://mlsanity.com/paper/2003.00196/) (2020)
- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing](https://mlsanity.com/paper/2011.15126/) (2020)
- [SimSwap: An Efficient Framework For High Fidelity Face Swapping](https://mlsanity.com/paper/2106.06340/) (2021)
- [FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping](https://mlsanity.com/paper/1912.13457/) (2019)
- [Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning](https://mlsanity.com/paper/2004.11660/) (2020)
- [Thin-Plate Spline Motion Model for Image Animation](https://mlsanity.com/paper/2203.14367/) (2022)
- [VFHQ: A High-Quality Dataset and Benchmark for Video Face Super-Resolution](https://mlsanity.com/paper/2205.03409/) (2022)
- [DisCo: Disentangled Control for Realistic Human Dance Generation](https://mlsanity.com/paper/2307.00040/) (2023)
- [MegaPortraits: One-shot Megapixel Neural Head Avatars](https://mlsanity.com/paper/2207.07621/) (2022)
- [One Shot Face Swapping on Megapixels](https://mlsanity.com/paper/2105.04932/) (2021)
- [MarioNETte: Few-shot Face Reenactment Preserving Identity of Unseen Targets](https://mlsanity.com/paper/1911.08139/) (2019)
- [DiffFace: Diffusion-based Face Swapping with Facial Guidance](https://mlsanity.com/paper/2212.13344/) (2022)
- [On Face Segmentation, Face Swapping, and Face Perception](https://mlsanity.com/paper/1704.06729/) (2017)
- [Fine-Grained Face Swapping via Regional GAN Inversion](https://mlsanity.com/paper/2211.14068/) (2022)
- [Face Adapter for Pre-Trained Diffusion Models with Fine-Grained ID and Attribute Control](https://mlsanity.com/paper/2405.12970/) (2024)
- [BlendFace: Re-designing Identity Encoders for Face-Swapping](https://mlsanity.com/paper/2307.10854/) (2023)

## Built on by (most cited first, 16 of 16)

- [DreamID-Omni: Unified Framework for Controllable Human-Centric Audio-Video Generation](https://mlsanity.com/paper/2602.12160/) (2026)
- [FilmWeaver: Weaving Consistent Multi-Shot Videos with Cache-Guided Autoregressive Diffusion](https://mlsanity.com/paper/2512.11274/) (2025)
- [DreamID-V:Bridging the Image-to-Video Gap for High-Fidelity Face Swapping via Diffusion Transformer](https://mlsanity.com/paper/2601.01425/) (2026)
- [ReMoT: Reinforcement Learning with Motion Contrast Triplets](https://mlsanity.com/paper/2603.00461/) (2026)
- [Beyond the Golden Data: Resolving the Motion-Vision Quality Dilemma via Timestep Selective Training](https://mlsanity.com/paper/2603.25527/) (2026)
- [CoInteract: Physically-Consistent Human-Object Interaction Video Synthesis via Spatially-Structured Co-Generation](https://mlsanity.com/paper/2604.19636/) (2026)
- [Preserving Source Video Realism: High-Fidelity Face Swapping for Cinematic Quality](https://mlsanity.com/paper/2512.07951/) (2025)
- [Towards High Fidelity Face Swapping: A Comprehensive Survey and New Benchmark](https://mlsanity.com/paper/2605.00883/) (2026)
- [Why Fake ? Unveiling the Semantic Vocabulary of Deepfake Detectors](https://mlsanity.com/paper/2607.07216/) (2026)
- [Layer-Aware Video Composition via Split-then-Merge](https://mlsanity.com/paper/2511.20809/) (2025)
- [GaussianSwap: Animatable Video Face Swapping with 3D Gaussian Splatting](https://mlsanity.com/paper/2601.05511/) (2026)
- [VFace: A Training-Free Approach for Diffusion-Based Video Face Swapping](https://mlsanity.com/paper/2602.07835/) (2026)
- [UniBioTransfer: A Unified Framework for Multiple Biometrics Transfer](https://mlsanity.com/paper/2603.19637/) (2026)
- [ID-Eraser: Proactive Defense Against Face Swapping via Identity Perturbation](https://mlsanity.com/paper/2604.21465/) (2026)
- [DataClaw0: Agentic Tailoring Multimodal Data from Raw Streams](https://mlsanity.com/paper/2606.21337/) (2026)
- [Adaptive Identity Anchoring: Closed-Loop Keyframe Placement for Synthetic Paired Supervision in Video Face Swapping](https://mlsanity.com/paper/2607.21434/) (2026)

## Abstract (arXiv)

Video face swapping aims to address two primary challenges: effectively transferring the source identity to the target video and accurately preserving the dynamic attributes of the target face, such as head poses, facial expressions, lip-sync, etc. Existing methods mainly focus on achieving high-quality identity transfer but often fall short in maintaining the dynamic attributes of the target face, leading to inconsistent results. We attribute this issue to the inherent coupling of facial appearance and motion in videos. To address this, we propose CanonSwap, a novel video face-swapping framework that decouples motion information from appearance information. Specifically, CanonSwap first eliminates motion-related information, enabling identity modification within a unified canonical space. Subsequently, the swapped feature is reintegrated into the original video space, ensuring the preservation of the target face's dynamic attributes. To further achieve precise identity transfer with minimal artifacts and enhanced realism, we design a Partial Identity Modulation module that adaptively integrates source identity features using a spatial mask to restrict modifications to facial regions. Additionally, we introduce several fine-grained synchronization metrics to comprehensively evaluate the performance of video face swapping methods. Extensive experiments demonstrate that our method significantly outperforms existing approaches in terms of visual quality, temporal consistency, and identity preservation. Our project page are publicly available at https://luoxyhappy.github.io/CanonSwap/.