# ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes

arXiv: https://arxiv.org/abs/1803.10562 · HTML: https://mlsanity.com/paper/1803.10562/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2018.

## Built on by (most cited first, 30 of 50)

- [Compositional Visual Generation with Composable Diffusion Models](https://mlsanity.com/paper/2206.01714/) (2022)
- [More Control for Free! Image Synthesis with Semantic Diffusion Guidance](https://mlsanity.com/paper/2112.05744/) (2021)
- [DCFace: Synthetic Face Generation with Dual Condition Diffusion Model](https://mlsanity.com/paper/2304.07060/) (2023)
- [Progressive Disentangled Representation Learning for Fine-Grained Controllable Talking Head Synthesis](https://mlsanity.com/paper/2211.14506/) (2022)
- [Image-to-image Translation via Hierarchical Style Disentanglement](https://mlsanity.com/paper/2103.01456/) (2021)
- [WarpedGANSpace: Finding non-linear RBF paths in GAN latent space](https://mlsanity.com/paper/2109.13357/) (2021)
- [Learning Disentangled Representations in the Imaging Domain](https://mlsanity.com/paper/2108.12043/) (2021)
- [Towards Relatable Explainable AI with the Perceptual Process](https://mlsanity.com/paper/2112.14005/) (2021)
- [Controllable and Guided Face Synthesis for Unconstrained Face Recognition](https://mlsanity.com/paper/2207.10180/) (2022)
- [Diverse Inpainting and Editing with GAN Inversion](https://mlsanity.com/paper/2307.15033/) (2023)
- [VecGAN: Image-to-Image Translation with Interpretable Latent Directions](https://mlsanity.com/paper/2207.03411/) (2022)
- [3D-FM GAN: Towards 3D-Controllable Face Manipulation](https://mlsanity.com/paper/2208.11257/) (2022)
- [GAN-based Facial Attribute Manipulation](https://mlsanity.com/paper/2210.12683/) (2022)
- [Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval](https://mlsanity.com/paper/2107.06256/) (2021)
- [Image-to-Image Translation with Disentangled Latent Vectors for Face Editing](https://mlsanity.com/paper/2301.04628/) (2023)
- [Learning Contrastive Representation for Semantic Correspondence](https://mlsanity.com/paper/2109.10967/) (2021)
- [Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data](https://mlsanity.com/paper/2203.10474/) (2022)
- [Disentangled Representation with Dual-stage Feature Learning for Face Anti-spoofing](https://mlsanity.com/paper/2110.09157/) (2021)
- [Federated Generalized Face Presentation Attack Detection](https://mlsanity.com/paper/2104.06595/) (2021)
- [Reference-Based 3D-Aware Image Editing with Triplanes](https://mlsanity.com/paper/2404.03632/) (2024)
- [Face De-identification: State-of-the-art Methods and Comparative Studies](https://mlsanity.com/paper/2411.09863/) (2024)
- [IA-FaceS: A Bidirectional Method for Semantic Face Editing](https://mlsanity.com/paper/2203.13097/) (2022)
- [Sill-Net: Feature Augmentation with Separated Illumination Representation](https://mlsanity.com/paper/2102.03539/) (2021)
- [FACEMUG: A Multimodal Generative and Fusion Framework for Local Facial Editing](https://mlsanity.com/paper/2412.19009/) (2024)
- [FacialGAN: Style Transfer and Attribute Manipulation on Synthetic Faces](https://mlsanity.com/paper/2110.09425/) (2021)
- [Encouraging Disentangled and Convex Representation with Controllable Interpolation Regularization](https://mlsanity.com/paper/2112.03163/) (2021)
- [Do Inpainting Yourself: Generative Facial Inpainting Guided by Exemplars](https://mlsanity.com/paper/2202.06358/) (2022)
- [DiffuseGAE: Controllable and High-fidelity Image Manipulation from Disentangled Representation](https://mlsanity.com/paper/2307.05899/) (2023)
- [On the Biometric Capacity of Generative Face Models](https://mlsanity.com/paper/2308.02065/) (2023)
- [A comprehensive survey on semantic facial attribute editing using generative adversarial networks](https://mlsanity.com/paper/2205.10587/) (2022)

## Abstract (arXiv)

Recent studies on face attribute transfer have achieved great success. A lot of models are able to transfer face attributes with an input image. However, they suffer from three limitations: (1) incapability of generating image by exemplars; (2) being unable to transfer multiple face attributes simultaneously; (3) low quality of generated images, such as low-resolution or artifacts. To address these limitations, we propose a novel model which receives two images of opposite attributes as inputs. Our model can transfer exactly the same type of attributes from one image to another by exchanging certain part of their encodings. All the attributes are encoded in a disentangled manner in the latent space, which enables us to manipulate several attributes simultaneously. Besides, our model learns the residual images so as to facilitate training on higher resolution images. With the help of multi-scale discriminators for adversarial training, it can even generate high-quality images with finer details and less artifacts. We demonstrate the effectiveness of our model on overcoming the above three limitations by comparing with other methods on the CelebA face database. A pytorch implementation is available at https://github.com/Prinsphield/ELEGANT.