# Geometry-Contrastive GAN for Facial Expression Transfer

arXiv: https://arxiv.org/abs/1802.01822 · HTML: https://mlsanity.com/paper/1802.01822/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2018.

## Built on by (most cited first, 16 of 16)

- [Cross-Modal Contrastive Learning for Text-to-Image Generation](https://mlsanity.com/paper/2101.04702/) (2021)
- [MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusion](https://mlsanity.com/paper/2311.12052/) (2023)
- [GUAVA: Generalizable Upper Body 3D Gaussian Avatar](https://mlsanity.com/paper/2505.03351/) (2025)
- [Facial Expression Translation using Landmark Guided GANs](https://mlsanity.com/paper/2209.02136/) (2022)
- [FastGHA: Generalized Few-Shot 3D Gaussian Head Avatars with Real-Time Animation](https://mlsanity.com/paper/2601.13837/) (2026)
- [A Review of Affective Generation Models](https://mlsanity.com/paper/2202.10763/) (2022)
- [Facial Prior Based First Order Motion Model for Micro-expression Generation](https://mlsanity.com/paper/2308.04536/) (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)
- [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)
- [F3A-GAN: Facial Flow for Face Animation with Generative Adversarial Networks](https://mlsanity.com/paper/2205.06204/) (2022)
- [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)
- [EMOSH: Expressive Motion and Shape Disentanglement for Human Animation](https://mlsanity.com/paper/2606.28026/) (2026)

## Abstract (arXiv)

In this paper, we propose a Geometry-Contrastive Generative Adversarial Network (GC-GAN) for transferring continuous emotions across different subjects. Given an input face with certain emotion and a target facial expression from another subject, GC-GAN can generate an identity-preserving face with the target expression. Geometry information is introduced into cGANs as continuous conditions to guide the generation of facial expressions. In order to handle the misalignment across different subjects or emotions, contrastive learning is used to transform geometry manifold into an embedded semantic manifold of facial expressions. Therefore, the embedded geometry is injected into the latent space of GANs and control the emotion generation effectively. Experimental results demonstrate that our proposed method can be applied in facial expression transfer even there exist big differences in facial shapes and expressions between different subjects.