# ExprGAN: Facial Expression Editing with Controllable Expression Intensity

arXiv: https://arxiv.org/abs/1709.03842 · HTML: https://mlsanity.com/paper/1709.03842/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2017.

## Built on by (most cited first, 30 of 47)

- [Audio-Driven Emotional Video Portraits](https://mlsanity.com/paper/2104.07452/) (2021)
- [Emotional Speech-Driven Animation with Content-Emotion Disentanglement](https://mlsanity.com/paper/2306.08990/) (2023)
- [Countering Malicious DeepFakes: Survey, Battleground, and Horizon](https://mlsanity.com/paper/2103.00218/) (2021)
- [Deepfake Detection: A Comprehensive Survey from the Reliability Perspective](https://mlsanity.com/paper/2211.10881/) (2022)
- [High-fidelity Generalized Emotional Talking Face Generation with Multi-modal Emotion Space Learning](https://mlsanity.com/paper/2305.02572/) (2023)
- [MixAugment & Mixup: Augmentation Methods for Facial Expression Recognition](https://mlsanity.com/paper/2205.04442/) (2022)
- [Neural Emotion Director: Speech-preserving semantic control of facial expressions in "in-the-wild" videos](https://mlsanity.com/paper/2112.00585/) (2021)
- [On the use of automatically generated synthetic image datasets for benchmarking face recognition](https://mlsanity.com/paper/2106.04215/) (2021)
- [DREAM-Talk: Diffusion-based Realistic Emotional Audio-driven Method for Single Image Talking Face Generation](https://mlsanity.com/paper/2312.13578/) (2023)
- [Discrete Point-wise Attack Is Not Enough: Generalized Manifold Adversarial Attack for Face Recognition](https://mlsanity.com/paper/2301.06083/) (2022)
- [Continuously Controllable Facial Expression Editing in Talking Face Videos](https://mlsanity.com/paper/2209.08289/) (2022)
- [Facial Affect Analysis: Learning from Synthetic Data & Multi-Task Learning Challenges](https://mlsanity.com/paper/2207.09748/) (2022)
- [Where Do Deep Fakes Look? Synthetic Face Detection via Gaze Tracking](https://mlsanity.com/paper/2101.01165/) (2021)
- [Affective Computing Has Changed: The Foundation Model Disruption](https://mlsanity.com/paper/2409.08907/) (2024)
- [A Review of Human Emotion Synthesis Based on Generative Technology](https://mlsanity.com/paper/2412.07116/) (2024)
- [MagicFace: High-Fidelity Facial Expression Editing with Action-Unit Control](https://mlsanity.com/paper/2501.02260/) (2025)
- [Contrastive Decoupled Representation Learning and Regularization for Speech-Preserving Facial Expression Manipulation](https://mlsanity.com/paper/2504.05672/) (2025)
- [Photorealistic and Identity-Preserving Image-Based Emotion Manipulation with Latent Diffusion Models](https://mlsanity.com/paper/2308.03183/) (2023)
- [Morphset:Augmenting categorical emotion datasets with dimensional affect labels using face morphing](https://mlsanity.com/paper/2103.02854/) (2021)
- [US-GAN: On the importance of Ultimate Skip Connection for Facial Expression Synthesis](https://mlsanity.com/paper/2112.13002/) (2021)
- ["Does it come in black?" CLIP-like models are zero-shot recommenders](https://mlsanity.com/paper/2204.02473/) (2022)
- [Dynamic Neural Textures: Generating Talking-Face Videos with Continuously Controllable Expressions](https://mlsanity.com/paper/2204.06180/) (2022)
- [Facial Expression Video Generation Based-On Spatio-temporal Convolutional GAN: FEV-GAN](https://mlsanity.com/paper/2210.11182/) (2022)
- [FaceCrafter: Identity-Conditional Diffusion with Disentangled Control over Facial Pose, Expression, and Emotion](https://mlsanity.com/paper/2505.15313/) (2025)
- [A Review of Affective Generation Models](https://mlsanity.com/paper/2202.10763/) (2022)
- [SARGAN: Spatial Attention-based Residuals for Facial Expression Manipulation](https://mlsanity.com/paper/2303.17212/) (2023)
- [NeRF-3DTalker: Neural Radiance Field with 3D Prior Aided Audio Disentanglement for Talking Head Synthesis](https://mlsanity.com/paper/2502.14178/) (2025)
- [Learning Spatial-Temporal Coherent Correlations for Speech-Preserving Facial Expression Manipulation](https://mlsanity.com/paper/2604.20226/) (2026)
- [Automated Deepfake Detection](https://mlsanity.com/paper/2106.10705/) (2021)
- [Altering Facial Expression Based on Textual Emotion](https://mlsanity.com/paper/2112.01454/) (2021)

## Abstract (arXiv)

Facial expression editing is a challenging task as it needs a high-level semantic understanding of the input face image. In conventional methods, either paired training data is required or the synthetic face resolution is low. Moreover, only the categories of facial expression can be changed. To address these limitations, we propose an Expression Generative Adversarial Network (ExprGAN) for photo-realistic facial expression editing with controllable expression intensity. An expression controller module is specially designed to learn an expressive and compact expression code in addition to the encoder-decoder network. This novel architecture enables the expression intensity to be continuously adjusted from low to high. We further show that our ExprGAN can be applied for other tasks, such as expression transfer, image retrieval, and data augmentation for training improved face expression recognition models. To tackle the small size of the training database, an effective incremental learning scheme is proposed. Quantitative and qualitative evaluations on the widely used Oulu-CASIA dataset demonstrate the effectiveness of ExprGAN.