# Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model

arXiv: https://arxiv.org/abs/1804.03675 · HTML: https://mlsanity.com/paper/1804.03675/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2018.

## Built on by (most cited first, 28 of 28)

- [Fake It Till You Make It: Face analysis in the wild using synthetic data alone](https://mlsanity.com/paper/2109.15102/) (2021)
- [Next3D: Generative Neural Texture Rasterization for 3D-Aware Head Avatars](https://mlsanity.com/paper/2211.11208/) (2022)
- [DCFace: Synthetic Face Generation with Dual Condition Diffusion Model](https://mlsanity.com/paper/2304.07060/) (2023)
- [AniFaceGAN: Animatable 3D-Aware Face Image Generation for Video Avatars](https://mlsanity.com/paper/2210.06465/) (2022)
- [Fast-GANFIT: Generative Adversarial Network for High Fidelity 3D Face Reconstruction](https://mlsanity.com/paper/2105.07474/) (2021)
- [FaceController: Controllable Attribute Editing for Face in the Wild](https://mlsanity.com/paper/2102.11464/) (2021)
- [SOGAN: 3D-Aware Shadow and Occlusion Robust GAN for Makeup Transfer](https://mlsanity.com/paper/2104.10567/) (2021)
- [Controllable and Guided Face Synthesis for Unconstrained Face Recognition](https://mlsanity.com/paper/2207.10180/) (2022)
- [InvertAvatar: Incremental GAN Inversion for Generalized Head Avatars](https://mlsanity.com/paper/2312.02222/) (2023)
- [Neuromorphic Event-based Facial Expression Recognition](https://mlsanity.com/paper/2304.06351/) (2023)
- [Cross-Domain and Disentangled Face Manipulation with 3D Guidance](https://mlsanity.com/paper/2104.11228/) (2021)
- [50 Years of Automated Face Recognition](https://mlsanity.com/paper/2505.24247/) (2025)
- [Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art](https://mlsanity.com/paper/2308.16316/) (2023)
- [Data Synthesis with Diverse Styles for Face Recognition via 3DMM-Guided Diffusion](https://mlsanity.com/paper/2504.00430/) (2025)
- [Simulated Adversarial Testing of Face Recognition Models](https://mlsanity.com/paper/2106.04569/) (2021)
- [PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape Estimation](https://mlsanity.com/paper/2303.07337/) (2023)
- [Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance](https://mlsanity.com/paper/2208.14263/) (2022)
- [DisCO: Portrait Distortion Correction with Perspective-Aware 3D GANs](https://mlsanity.com/paper/2302.12253/) (2023)
- [Recent Advances in Deep Learning Techniques for Face Recognition](https://mlsanity.com/paper/2103.10492/) (2021)
- [SeCGAN: Parallel Conditional Generative Adversarial Networks for Face Editing via Semantic Consistency](https://mlsanity.com/paper/2111.09298/) (2021)
- [3D Dense Face Alignment with Fused Features by Aggregating CNNs and GCNs](https://mlsanity.com/paper/2203.04643/) (2022)
- [3D-aware Image Generation and Editing with Multi-modal Conditions](https://mlsanity.com/paper/2403.06470/) (2024)
- [Label Geometry Aware Discriminator for Conditional Generative Networks](https://mlsanity.com/paper/2105.05501/) (2021)
- [Pixel Sampling for Style Preserving Face Pose Editing](https://mlsanity.com/paper/2106.07310/) (2021)
- [Brand Label Albedo Extraction of eCommerce Products using Generative Adversarial Network](https://mlsanity.com/paper/2109.02929/) (2021)
- [A Unified Architecture of Semantic Segmentation and Hierarchical Generative Adversarial Networks for Expression Manipulation](https://mlsanity.com/paper/2112.04603/) (2021)
- [Efficient Hair Style Transfer with Generative Adversarial Networks](https://mlsanity.com/paper/2210.12524/) (2022)
- [Designing and Generating Diverse, Equitable Face Image Datasets for Face Verification Tasks](https://mlsanity.com/paper/2511.17393/) (2025)

## Abstract (arXiv)

We propose a novel end-to-end semi-supervised adversarial framework to generate photorealistic face images of new identities with wide ranges of expressions, poses, and illuminations conditioned by a 3D morphable model. Previous adversarial style-transfer methods either supervise their networks with large volume of paired data or use unpaired data with a highly under-constrained two-way generative framework in an unsupervised fashion. We introduce pairwise adversarial supervision to constrain two-way domain adaptation by a small number of paired real and synthetic images for training along with the large volume of unpaired data. Extensive qualitative and quantitative experiments are performed to validate our idea. Generated face images of new identities contain pose, lighting and expression diversity and qualitative results show that they are highly constraint by the synthetic input image while adding photorealism and retaining identity information. We combine face images generated by the proposed method with the real data set to train face recognition algorithms. We evaluated the model on two challenging data sets: LFW and IJB-A. We observe that the generated images from our framework consistently improves over the performance of deep face recognition network trained with Oxford VGG Face dataset and achieves comparable results to the state-of-the-art.