# A 3D GAN for Improved Large-pose Facial Recognition

arXiv: https://arxiv.org/abs/2012.10545 · HTML: https://mlsanity.com/paper/2012.10545/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2020.

## Built on by (most cited first, 19 of 19)

- [ClipFace: Text-guided Editing of Textured 3D Morphable Models](https://mlsanity.com/paper/2212.01406/) (2022)
- [Synthetic Data in Human Analysis: A Survey](https://mlsanity.com/paper/2208.09191/) (2022)
- [On the use of automatically generated synthetic image datasets for benchmarking face recognition](https://mlsanity.com/paper/2106.04215/) (2021)
- [Federated Learning for Face Recognition with Gradient Correction](https://mlsanity.com/paper/2112.07246/) (2021)
- [Underwater Light Field Retention : Neural Rendering for Underwater Imaging](https://mlsanity.com/paper/2203.11006/) (2022)
- [Simulated Adversarial Testing of Face Recognition Models](https://mlsanity.com/paper/2106.04569/) (2021)
- [How to Boost Face Recognition with StyleGAN?](https://mlsanity.com/paper/2210.10090/) (2022)
- [PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape Estimation](https://mlsanity.com/paper/2303.07337/) (2023)
- [Unsupervised High-Fidelity Facial Texture Generation and Reconstruction](https://mlsanity.com/paper/2110.04760/) (2021)
- [InstaFace: Identity-Preserving Facial Editing with Single Image Inference](https://mlsanity.com/paper/2502.20577/) (2025)
- [Human Silhouette and Skeleton Video Synthesis through Wi-Fi signals](https://mlsanity.com/paper/2203.05864/) (2022)
- [Revisiting GANs by Best-Response Constraint: Perspective, Methodology, and Application](https://mlsanity.com/paper/2205.10146/) (2022)
- [Comparing Synthetic Tabular Data Generation Between a Probabilistic Model and a Deep Learning Model for Education Use Cases](https://mlsanity.com/paper/2210.08528/) (2022)
- [LAPP: Layer Adaptive Progressive Pruning for Compressing CNNs from Scratch](https://mlsanity.com/paper/2309.14157/) (2023)
- [Combating Semantic Contamination in Learning with Label Noise](https://mlsanity.com/paper/2412.11620/) (2024)
- [MUNCH: Modelling Unique 'N Controllable Heads](https://mlsanity.com/paper/2310.02753/) (2023)
- [LSP Framework: A Compensatory Model for Defeating Trigger Reverse Engineering via Label Smoothing Poisoning](https://mlsanity.com/paper/2404.12852/) (2024)
- [Sampling Strategies for Mitigating Bias in Face Synthesis Methods](https://mlsanity.com/paper/2405.11320/) (2024)
- [Leveraging Dissimilarity Invariance as a Robust Anchor for Learning with Noisy Labels](https://mlsanity.com/paper/2607.17857/) (2026)

## Abstract (arXiv)

Facial recognition using deep convolutional neural networks relies on the availability of large datasets of face images. Many examples of identities are needed, and for each identity, a large variety of images are needed in order for the network to learn robustness to intra-class variation. In practice, such datasets are difficult to obtain, particularly those containing adequate variation of pose. Generative Adversarial Networks (GANs) provide a potential solution to this problem due to their ability to generate realistic, synthetic images. However, recent studies have shown that current methods of disentangling pose from identity are inadequate. In this work we incorporate a 3D morphable model into the generator of a GAN in order to learn a nonlinear texture model from in-the-wild images. This allows generation of new, synthetic identities, and manipulation of pose, illumination and expression without compromising the identity. Our synthesised data is used to augment training of facial recognition networks with performance evaluated on the challenging CFP and CPLFW datasets.