# Geometry Guided Adversarial Facial Expression Synthesis

arXiv: https://arxiv.org/abs/1712.03474 · HTML: https://mlsanity.com/paper/1712.03474/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2017.

## Built on by (most cited first, 29 of 29)

- [Image-to-Image Translation: Methods and Applications](https://mlsanity.com/paper/2101.08629/) (2021)
- [DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation](https://mlsanity.com/paper/2207.06124/) (2022)
- [A Study of the Human Perception of Synthetic Faces](https://mlsanity.com/paper/2111.04230/) (2021)
- [Discrete Point-wise Attack Is Not Enough: Generalized Manifold Adversarial Attack for Face Recognition](https://mlsanity.com/paper/2301.06083/) (2022)
- [Image-to-Video Generation via 3D Facial Dynamics](https://mlsanity.com/paper/2105.14678/) (2021)
- [Total Generate: Cycle in Cycle Generative Adversarial Networks for Generating Human Faces, Hands, Bodies, and Natural Scenes](https://mlsanity.com/paper/2106.10876/) (2021)
- [Learning Disentangled Representation for One-shot Progressive Face Swapping](https://mlsanity.com/paper/2203.12985/) (2022)
- [Universal Facial Encoding of Codec Avatars from VR Headsets](https://mlsanity.com/paper/2407.13038/) (2024)
- [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)
- [Facial Expression Video Generation Based-On Spatio-temporal Convolutional GAN: FEV-GAN](https://mlsanity.com/paper/2210.11182/) (2022)
- [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)
- [Altering Facial Expression Based on Textual Emotion](https://mlsanity.com/paper/2112.01454/) (2021)
- [2CET-GAN: Pixel-Level GAN Model for Human Facial Expression Transfer](https://mlsanity.com/paper/2211.11570/) (2022)
- [Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection](https://mlsanity.com/paper/2212.05667/) (2022)
- [Exp-Graph: How Connections Learn Facial Attributes in Graph-based Expression Recognition](https://mlsanity.com/paper/2507.14608/) (2025)
- [Perceptually Validated Precise Local Editing for Facial Action Units with StyleGAN](https://mlsanity.com/paper/2107.12143/) (2021)
- [ChildPredictor: A Child Face Prediction Framework with Disentangled Learning](https://mlsanity.com/paper/2204.09962/) (2022)
- [F3A-GAN: Facial Flow for Face Animation with Generative Adversarial Networks](https://mlsanity.com/paper/2205.06204/) (2022)
- [Semantic-aware One-shot Face Re-enactment with Dense Correspondence Estimation](https://mlsanity.com/paper/2211.12674/) (2022)
- [GaFET: Learning Geometry-aware Facial Expression Translation from In-The-Wild Images](https://mlsanity.com/paper/2308.03413/) (2023)
- [FacEnhance: Facial Expression Enhancing with Recurrent DDPMs](https://mlsanity.com/paper/2406.09040/) (2024)
- [Emotion Transfer Using Vector-Valued Infinite Task Learning](https://mlsanity.com/paper/2102.05075/) (2021)
- [A Unified Architecture of Semantic Segmentation and Hierarchical Generative Adversarial Networks for Expression Manipulation](https://mlsanity.com/paper/2112.04603/) (2021)
- [Semi-Supervised Image-to-Image Translation using Latent Space Mapping](https://mlsanity.com/paper/2203.15241/) (2022)
- [Face Animation with Multiple Source Images](https://mlsanity.com/paper/2212.00256/) (2022)
- [High-Fidelity 3D Facial Avatar Synthesis with Controllable Fine-Grained Expressions](https://mlsanity.com/paper/2603.14781/) (2026)
- [PartNerFace: Part-based Neural Radiance Fields for Animatable Facial Avatar Reconstruction](https://mlsanity.com/paper/2604.13918/) (2026)

## Abstract (arXiv)

Facial expression synthesis has drawn much attention in the field of computer graphics and pattern recognition. It has been widely used in face animation and recognition. However, it is still challenging due to the high-level semantic presence of large and non-linear face geometry variations. This paper proposes a Geometry-Guided Generative Adversarial Network (G2-GAN) for photo-realistic and identity-preserving facial expression synthesis. We employ facial geometry (fiducial points) as a controllable condition to guide facial texture synthesis with specific expression. A pair of generative adversarial subnetworks are jointly trained towards opposite tasks: expression removal and expression synthesis. The paired networks form a mapping cycle between neutral expression and arbitrary expressions, which also facilitate other applications such as face transfer and expression invariant face recognition. Experimental results show that our method can generate compelling perceptual results on various facial expression synthesis databases. An expression invariant face recognition experiment is also performed to further show the advantages of our proposed method.