# SwiftAvatar: Efficient Auto-Creation of Parameterized Stylized Character on Arbitrary Avatar Engines

arXiv: https://arxiv.org/abs/2301.08153 · HTML: https://mlsanity.com/paper/2301.08153/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 31 references that are in the map.
- First posted on arXiv 19 Jan 2023.

## Builds on (most cited first, 25 of 25)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Generating Diverse High-Fidelity Images with VQ-VAE-2](https://mlsanity.com/paper/1906.00446/) (2019)
- [Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation](https://mlsanity.com/paper/2008.00951/) (2020)
- [Which Training Methods for GANs do actually Converge?](https://mlsanity.com/paper/1801.04406/) (2018)
- [MaskGAN: Towards Diverse and Interactive Facial Image Manipulation](https://mlsanity.com/paper/1907.11922/) (2019)
- [Symmetric Cross Entropy for Robust Learning with Noisy Labels](https://mlsanity.com/paper/1908.06112/) (2019)
- [In-Domain GAN Inversion for Real Image Editing](https://mlsanity.com/paper/2004.00049/) (2020)
- [Image2StyleGAN++: How to Edit the Embedded Images?](https://mlsanity.com/paper/1911.11544/) (2019)
- [Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network](https://mlsanity.com/paper/1803.07835/) (2018)
- [Morphable Face Models - An Open Framework](https://mlsanity.com/paper/1709.08398/) (2017)
- [Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network](https://mlsanity.com/paper/1612.04904/) (2016)
- [SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing](https://mlsanity.com/paper/2112.02236/) (2021)
- [End-to-end 3D face reconstruction with deep neural networks](https://mlsanity.com/paper/1704.05020/) (2017)
- [Unsupervised Image-to-Image Translation via Pre-trained StyleGAN2 Network](https://mlsanity.com/paper/2010.05713/) (2020)
- [Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild](https://mlsanity.com/paper/1803.05536/) (2018)
- [Fast and Robust Face-to-Parameter Translation for Game Character Auto-Creation](https://mlsanity.com/paper/2008.07132/) (2020)
- [Face-to-Parameter Translation for Game Character Auto-Creation](https://mlsanity.com/paper/1909.01064/) (2019)
- [Fine-Tuning StyleGAN2 For Cartoon Face Generation](https://mlsanity.com/paper/2106.12445/) (2021)
- [AgileAvatar: Stylized 3D Avatar Creation via Cascaded Domain Bridging](https://mlsanity.com/paper/2211.07818/) (2022)
- [Unsupervised Creation of Parameterized Avatars](https://mlsanity.com/paper/1704.05693/) (2017)

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

- [Snapmoji: Instant Generation of Animatable Dual-Stylized Avatars](https://mlsanity.com/paper/2503.11978/) (2025)
- [Biometrics in Extended Reality: A Review](https://mlsanity.com/paper/2411.10489/) (2024)
- [EasyCraft: A Robust and Efficient Framework for Automatic Avatar Crafting](https://mlsanity.com/paper/2503.01158/) (2025)

## Abstract (arXiv)

The creation of a parameterized stylized character involves careful selection of numerous parameters, also known as the "avatar vectors" that can be interpreted by the avatar engine. Existing unsupervised avatar vector estimation methods that auto-create avatars for users, however, often fail to work because of the domain gap between realistic faces and stylized avatar images. To this end, we propose SwiftAvatar, a novel avatar auto-creation framework that is evidently superior to previous works. SwiftAvatar introduces dual-domain generators to create pairs of realistic faces and avatar images using shared latent codes. The latent codes can then be bridged with the avatar vectors as pairs, by performing GAN inversion on the avatar images rendered from the engine using avatar vectors. Through this way, we are able to synthesize paired data in high-quality as many as possible, consisting of avatar vectors and their corresponding realistic faces. We also propose semantic augmentation to improve the diversity of synthesis. Finally, a light-weight avatar vector estimator is trained on the synthetic pairs to implement efficient auto-creation. Our experiments demonstrate the effectiveness and efficiency of SwiftAvatar on two different avatar engines. The superiority and advantageous flexibility of SwiftAvatar are also verified in both subjective and objective evaluations.