# StyleGAN3: Generative Networks for Improving the Equivariance of Translation and Rotation

arXiv: https://arxiv.org/abs/2307.03898 · HTML: https://mlsanity.com/paper/2307.03898/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 14 references that are in the map.
- First posted on arXiv 8 Jul 2023.

## Builds on (most cited first, 11 of 11)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [StyleGAN2 Distillation for Feed-forward Image Manipulation](https://mlsanity.com/paper/2003.03581/) (2020)
- [Third Time's the Charm? Image and Video Editing with StyleGAN3](https://mlsanity.com/paper/2201.13433/) (2022)
- [StyleFaceV: Face Video Generation via Decomposing and Recomposing Pretrained StyleGAN3](https://mlsanity.com/paper/2208.07862/) (2022)
- [Texture Representation via Analysis and Synthesis with Generative Adversarial Networks](https://mlsanity.com/paper/2212.09983/) (2022)

## Built on by (most cited first, 3 of 3)

- [Synthesizing Iris Images using Generative Adversarial Networks: Survey and Comparative Analysis](https://mlsanity.com/paper/2404.17105/) (2024)
- [MFFI: Multi-Dimensional Face Forgery Image Dataset for Real-World Scenarios](https://mlsanity.com/paper/2509.05592/) (2025)
- [Unleashing MLLMs on the Edge: A Unified Framework for Cross-Modal ReID via Adaptive SVD Distillation](https://mlsanity.com/paper/2602.12936/) (2026)

## Abstract (arXiv)

StyleGAN can use style to affect facial posture and identity features, and noise to affect hair, wrinkles, skin color and other details. Among these, the outcomes of the picture processing will vary slightly between different versions of styleGAN. As a result, the comparison of performance differences between styleGAN2 and the two modified versions of styleGAN3 will be the main focus of this study. We used the FFHQ dataset as the dataset and FID, EQ-T, and EQ-R were used to be the assessment of the model. In the end, we discovered that Stylegan3 version is a better generative network to improve the equivariance. Our findings have a positive impact on the creation of animation and videos.