# GANmut: Generating and Modifying Facial Expressions

arXiv: https://arxiv.org/abs/2406.11079 · HTML: https://mlsanity.com/paper/2406.11079/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 56 references that are in the map.
- First posted on arXiv 16 Jun 2024.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Interpreting the Latent Space of GANs for Semantic Face Editing](https://mlsanity.com/paper/1907.10786/) (2019)
- [GANSpace: Discovering Interpretable GAN Controls](https://mlsanity.com/paper/2004.02546/) (2020)
- [Conditional Image Synthesis With Auxiliary Classifier GANs](https://mlsanity.com/paper/1610.09585/) (2016)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Unsupervised Discovery of Interpretable Directions in the GAN Latent Space](https://mlsanity.com/paper/2002.03754/) (2020)
- [Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace](https://mlsanity.com/paper/1910.04855/) (2019)
- [Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond](https://mlsanity.com/paper/1804.10938/) (2018)
- [Analysing Affective Behavior in the First ABAW 2020 Competition](https://mlsanity.com/paper/2001.11409/) (2020)
- [Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework](https://mlsanity.com/paper/2103.15792/) (2021)
- [Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study](https://mlsanity.com/paper/2105.03790/) (2021)
- [Analysing Affective Behavior in the second ABAW2 Competition](https://mlsanity.com/paper/2106.15318/) (2021)
- [GANimation: Anatomically-aware Facial Animation from a Single Image](https://mlsanity.com/paper/1807.09251/) (2018)
- [Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network](https://mlsanity.com/paper/1910.11111/) (2019)
- [ABAW: Learning from Synthetic Data & Multi-Task Learning Challenges](https://mlsanity.com/paper/2207.01138/) (2022)
- [ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challenges](https://mlsanity.com/paper/2303.01498/) (2023)
- [The 6th Affective Behavior Analysis in-the-wild (ABAW) Competition](https://mlsanity.com/paper/2402.19344/) (2024)
- [Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond](https://mlsanity.com/paper/2401.01219/) (2024)
- [A Deep Neural Architecture for Harmonizing 3-D Input Data Analysis and Decision Making in Medical Imaging](https://mlsanity.com/paper/2303.00175/) (2023)
- [MixAugment & Mixup: Augmentation Methods for Facial Expression Recognition](https://mlsanity.com/paper/2205.04442/) (2022)
- [COVID-19 Computer-aided Diagnosis through AI-assisted CT Imaging Analysis: Deploying a Medical AI System](https://mlsanity.com/paper/2403.06242/) (2024)
- [SMIT: Stochastic Multi-Label Image-to-Image Translation](https://mlsanity.com/paper/1812.03704/) (2018)
- [BTDNet: a Multi-Modal Approach for Brain Tumor Radiogenomic Classification](https://mlsanity.com/paper/2310.03485/) (2023)
- [MMA-MRNNet: Harnessing Multiple Models of Affect and Dynamic Masked RNN for Precise Facial Expression Intensity Estimation](https://mlsanity.com/paper/2303.00180/) (2023)
- [SMILE: Semantically-guided Multi-attribute Image and Layout Editing](https://mlsanity.com/paper/2010.02315/) (2020)

## Abstract (arXiv)

In the realm of emotion synthesis, the ability to create authentic and nuanced facial expressions continues to gain importance. The GANmut study discusses a recently introduced advanced GAN framework that, instead of relying on predefined labels, learns a dynamic and interpretable emotion space. This methodology maps each discrete emotion as vectors starting from a neutral state, their magnitude reflecting the emotion's intensity. The current project aims to extend the study of this framework by benchmarking across various datasets, image resolutions, and facial detection methodologies. This will involve conducting a series of experiments using two emotional datasets: Aff-Wild2 and AffNet. Aff-Wild2 contains videos captured in uncontrolled environments, which include diverse camera angles, head positions, and lighting conditions, providing a real-world challenge. AffNet offers images with labelled emotions, improving the diversity of emotional expressions available for training. The first two experiments will focus on training GANmut using the Aff-Wild2 dataset, processed with either RetinaFace or MTCNN, both of which are high-performance deep learning face detectors. This setup will help determine how well GANmut can learn to synthesise emotions under challenging conditions and assess the comparative effectiveness of these face detection technologies. The subsequent two experiments will merge the Aff-Wild2 and AffNet datasets, combining the real world variability of Aff-Wild2 with the diverse emotional labels of AffNet. The same face detectors, RetinaFace and MTCNN, will be employed to evaluate whether the enhanced diversity of the combined datasets improves GANmut's performance and to compare the impact of each face detection method in this hybrid setup.