# CAFE-GAN: Arbitrary Face Attribute Editing with Complementary Attention Feature

arXiv: https://arxiv.org/abs/2011.11900 · HTML: https://mlsanity.com/paper/2011.11900/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2020.

## Built on by (most cited first, 20 of 20)

- [High-Fidelity and Arbitrary Face Editing](https://mlsanity.com/paper/2103.15814/) (2021)
- [Disentangled Lifespan Face Synthesis](https://mlsanity.com/paper/2108.02874/) (2021)
- [Identity-Preserving Aging of Face Images via Latent Diffusion Models](https://mlsanity.com/paper/2307.08585/) (2023)
- [IA-FaceS: A Bidirectional Method for Semantic Face Editing](https://mlsanity.com/paper/2203.13097/) (2022)
- [Adverse Weather Image Translation with Asymmetric and Uncertainty-aware GAN](https://mlsanity.com/paper/2112.04283/) (2021)
- [Stellar: Systematic Evaluation of Human-Centric Personalized Text-to-Image Methods](https://mlsanity.com/paper/2312.06116/) (2023)
- [Unsupervised High-Resolution Portrait Gaze Correction and Animation](https://mlsanity.com/paper/2207.00256/) (2022)
- [A Survey of Multimodal Composite Editing and Retrieval](https://mlsanity.com/paper/2409.05405/) (2024)
- [Spatial-temporal Transformer-guided Diffusion based Data Augmentation for Efficient Skeleton-based Action Recognition](https://mlsanity.com/paper/2302.13434/) (2023)
- [A comprehensive survey on semantic facial attribute editing using generative adversarial networks](https://mlsanity.com/paper/2205.10587/) (2022)
- [Supervised Attribute Information Removal and Reconstruction for Image Manipulation](https://mlsanity.com/paper/2207.06555/) (2022)
- [Controlling the Latent Space of GANs through Reinforcement Learning: A Case Study on Task-based Image-to-Image Translation](https://mlsanity.com/paper/2307.13978/) (2023)
- [MixSyn: Learning Composition and Style for Multi-Source Image Synthesis](https://mlsanity.com/paper/2111.12705/) (2021)
- [Can GAN-induced Attribute Manipulations Impact Face Recognition?](https://mlsanity.com/paper/2209.02941/) (2022)
- [Mitigating the Impact of Attribute Editing on Face Recognition](https://mlsanity.com/paper/2403.08092/) (2024)
- [GAT-NeRF: Geometry-Aware-Transformer Enhanced Neural Radiance Fields for High-Fidelity 4D Facial Avatars](https://mlsanity.com/paper/2601.14875/) (2026)
- [Reference Guided Image Inpainting using Facial Attributes](https://mlsanity.com/paper/2301.08044/) (2023)
- [UP-FacE: User-predictable Fine-grained Face Shape Editing](https://mlsanity.com/paper/2403.13972/) (2024)
- [HAIFAI: Human-AI Interaction for Mental Face Reconstruction](https://mlsanity.com/paper/2412.06323/) (2024)
- [A Large-scale Evaluation of Text-guided Models for Facial Editing](https://mlsanity.com/paper/2608.28802/) (2026)

## Abstract (arXiv)

The goal of face attribute editing is altering a facial image according to given target attributes such as hair color, mustache, gender, etc. It belongs to the image-to-image domain transfer problem with a set of attributes considered as a distinctive domain. There have been some works in multi-domain transfer problem focusing on facial attribute editing employing Generative Adversarial Network (GAN). These methods have reported some successes but they also result in unintended changes in facial regions - meaning the generator alters regions unrelated to the specified attributes. To address this unintended altering problem, we propose a novel GAN model which is designed to edit only the parts of a face pertinent to the target attributes by the concept of Complementary Attention Feature (CAFE). CAFE identifies the facial regions to be transformed by considering both target attributes as well as complementary attributes, which we define as those attributes absent in the input facial image. In addition, we introduce a complementary feature matching to help in training the generator for utilizing the spatial information of attributes. Effectiveness of the proposed method is demonstrated by analysis and comparison study with state-of-the-art methods.