# Expression Conditional GAN for Facial Expression-to-Expression Translation

arXiv: https://arxiv.org/abs/1905.05416 · HTML: https://mlsanity.com/paper/1905.05416/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2019.

## Built on by (most cited first, 11 of 11)

- [Image-to-Image Translation: Methods and Applications](https://mlsanity.com/paper/2101.08629/) (2021)
- [A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing](https://mlsanity.com/paper/2110.01442/) (2021)
- [Reversible Graph Neural Network-based Reaction Distribution Learning for Multiple Appropriate Facial Reactions Generation](https://mlsanity.com/paper/2305.15270/) (2023)
- [Bipartite Graph Reasoning GANs for Person Pose and Facial Image Synthesis](https://mlsanity.com/paper/2211.06719/) (2022)
- [Layout-to-Image Translation with Double Pooling Generative Adversarial Networks](https://mlsanity.com/paper/2108.12900/) (2021)
- [Edge Guided GANs with Multi-Scale Contrastive Learning for Semantic Image Synthesis](https://mlsanity.com/paper/2307.12084/) (2023)
- [A Review of Human Emotion Synthesis Based on Generative Technology](https://mlsanity.com/paper/2412.07116/) (2024)
- [Facial Expression Translation using Landmark Guided GANs](https://mlsanity.com/paper/2209.02136/) (2022)
- [Enhanced Multi-Scale Cross-Attention for Person Image Generation](https://mlsanity.com/paper/2501.08900/) (2025)
- [F3A-GAN: Facial Flow for Face Animation with Generative Adversarial Networks](https://mlsanity.com/paper/2205.06204/) (2022)
- [GaFET: Learning Geometry-aware Facial Expression Translation from In-The-Wild Images](https://mlsanity.com/paper/2308.03413/) (2023)

## Abstract (arXiv)

In this paper, we focus on the facial expression translation task and propose a novel Expression Conditional GAN (ECGAN) which can learn the mapping from one image domain to another one based on an additional expression attribute. The proposed ECGAN is a generic framework and is applicable to different expression generation tasks where specific facial expression can be easily controlled by the conditional attribute label. Besides, we introduce a novel face mask loss to reduce the influence of background changing. Moreover, we propose an entire framework for facial expression generation and recognition in the wild, which consists of two modules, i.e., generation and recognition. Finally, we evaluate our framework on several public face datasets in which the subjects have different races, illumination, occlusion, pose, color, content and background conditions. Even though these datasets are very diverse, both the qualitative and quantitative results demonstrate that our approach is able to generate facial expressions accurately and robustly.