# Smooth-Swap: A Simple Enhancement for Face-Swapping with Smoothness

arXiv: https://arxiv.org/abs/2112.05907 · HTML: https://mlsanity.com/paper/2112.05907/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 39 references that are in the map.
- First posted on arXiv 11 Dec 2021.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [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)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [Which Training Methods for GANs do actually Converge?](https://mlsanity.com/paper/1801.04406/) (2018)
- [Face2Face: Real-time Face Capture and Reenactment of RGB Videos](https://mlsanity.com/paper/2007.14808/) (2020)
- [FSGAN: Subject Agnostic Face Swapping and Reenactment](https://mlsanity.com/paper/1908.05932/) (2019)
- [Deferred Neural Rendering: Image Synthesis using Neural Textures](https://mlsanity.com/paper/1904.12356/) (2019)
- [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)
- [Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision](https://mlsanity.com/paper/1905.06817/) (2019)
- [One Shot Face Swapping on Megapixels](https://mlsanity.com/paper/2105.04932/) (2021)
- [HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping](https://mlsanity.com/paper/2106.09965/) (2021)
- [Towards Open-Set Identity Preserving Face Synthesis](https://mlsanity.com/paper/1803.11182/) (2018)
- [On Face Segmentation, Face Swapping, and Face Perception](https://mlsanity.com/paper/1704.06729/) (2017)
- [RSGAN: Face Swapping and Editing using Face and Hair Representation in Latent Spaces](https://mlsanity.com/paper/1804.03447/) (2018)
- [FSNet: An Identity-Aware Generative Model for Image-based Face Swapping](https://mlsanity.com/paper/1811.12666/) (2018)

## Built on by (most cited first, 30 of 30)

- [DiffFace: Diffusion-based Face Swapping with Facial Guidance](https://mlsanity.com/paper/2212.13344/) (2022)
- [Fine-Grained Face Swapping via Regional GAN Inversion](https://mlsanity.com/paper/2211.14068/) (2022)
- [Deepfake Generation and Detection: A Benchmark and Survey](https://mlsanity.com/paper/2403.17881/) (2024)
- [BlendFace: Re-designing Identity Encoders for Face-Swapping](https://mlsanity.com/paper/2307.10854/) (2023)
- [CharacterFactory: Sampling Consistent Characters with GANs for Diffusion Models](https://mlsanity.com/paper/2404.15677/) (2024)
- [Controllable Inversion of Black-Box Face Recognition Models via Diffusion](https://mlsanity.com/paper/2303.13006/) (2023)
- [HS-Diffusion: Semantic-Mixing Diffusion for Head Swapping](https://mlsanity.com/paper/2212.06458/) (2022)
- [Reinforced Disentanglement for Face Swapping without Skip Connection](https://mlsanity.com/paper/2307.07928/) (2023)
- [Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook](https://mlsanity.com/paper/2411.19537/) (2024)
- [HiFiVFS: High Fidelity Video Face Swapping](https://mlsanity.com/paper/2411.18293/) (2024)
- [ReliableSwap: Boosting General Face Swapping Via Reliable Supervision](https://mlsanity.com/paper/2306.05356/) (2023)
- [E4S: Fine-grained Face Swapping via Editing With Regional GAN Inversion](https://mlsanity.com/paper/2310.15081/) (2023)
- [FlowFace: Semantic Flow-guided Shape-aware Face Swapping](https://mlsanity.com/paper/2212.02797/) (2022)
- [SelfSwapper: Self-Supervised Face Swapping via Shape Agnostic Masked AutoEncoder](https://mlsanity.com/paper/2402.07370/) (2024)
- [PrivateGaze: Preserving User Privacy in Black-box Mobile Gaze Tracking Services](https://mlsanity.com/paper/2408.00950/) (2024)
- [MFIM: Megapixel Facial Identity Manipulation](https://mlsanity.com/paper/2308.01536/) (2023)
- [Face Swap via Diffusion Model](https://mlsanity.com/paper/2403.01108/) (2024)
- [Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection](https://mlsanity.com/paper/2212.05667/) (2022)
- [FlowFace++: Explicit Semantic Flow-supervised End-to-End Face Swapping](https://mlsanity.com/paper/2306.12686/) (2023)
- [Generalized Deepfakes Detection with Reconstructed-Blended Images and Multi-scale Feature Reconstruction Network](https://mlsanity.com/paper/2312.08020/) (2023)
- [FaceTracer: Unveiling Source Identities from Swapped Face Images and Videos for Fraud Prevention](https://mlsanity.com/paper/2412.08082/) (2024)
- [Controllable and Expressive One-Shot Video Head Swapping](https://mlsanity.com/paper/2506.16852/) (2025)
- [LatentSwap: An Efficient Latent Code Mapping Framework for Face Swapping](https://mlsanity.com/paper/2402.18351/) (2024)
- [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)
- [Watch Out for the Confusing Faces: Detecting Face Swapping with the Probability Distribution of Face Identification Models](https://mlsanity.com/paper/2303.13131/) (2023)
- [SwapTalk: Audio-Driven Talking Face Generation with One-Shot Customization in Latent Space](https://mlsanity.com/paper/2405.05636/) (2024)
- [MVFNet: Multipurpose Video Forensics Network using Multiple Forms of Forensic Evidence](https://mlsanity.com/paper/2503.20991/) (2025)
- [PerFace: Metric Learning in Perceptual Facial Similarity for Enhanced Face Anonymization](https://mlsanity.com/paper/2509.20281/) (2025)
- [GSwap: Realistic Head Swapping with Dynamic Neural Gaussian Field](https://mlsanity.com/paper/2603.23168/) (2026)

## Abstract (arXiv)

Face-swapping models have been drawing attention for their compelling generation quality, but their complex architectures and loss functions often require careful tuning for successful training. We propose a new face-swapping model called `Smooth-Swap', which excludes complex handcrafted designs and allows fast and stable training. The main idea of Smooth-Swap is to build smooth identity embedding that can provide stable gradients for identity change. Unlike the one used in previous models trained for a purely discriminative task, the proposed embedding is trained with a supervised contrastive loss promoting a smoother space. With improved smoothness, Smooth-Swap suffices to be composed of a generic U-Net-based generator and three basic loss functions, a far simpler design compared with the previous models. Extensive experiments on face-swapping benchmarks (FFHQ, FaceForensics++) and face images in the wild show that our model is also quantitatively and qualitatively comparable or even superior to the existing methods.