# Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis

arXiv: https://arxiv.org/abs/1704.04086 · HTML: https://mlsanity.com/paper/1704.04086/

## Key facts

- Cited by 75 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2017.

## Built on by (most cited first, 30 of 75)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
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- [TriPlaneNet: An Encoder for EG3D Inversion](https://mlsanity.com/paper/2303.13497/) (2023)
- [CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation](https://mlsanity.com/paper/2107.02408/) (2021)
- [A survey of synthetic data augmentation methods in computer vision](https://mlsanity.com/paper/2403.10075/) (2024)
- [Collaging Class-specific GANs for Semantic Image Synthesis](https://mlsanity.com/paper/2110.04281/) (2021)
- [Regularising Inverse Problems with Generative Machine Learning Models](https://mlsanity.com/paper/2107.11191/) (2021)
- [Cascaded Cross MLP-Mixer GANs for Cross-View Image Translation](https://mlsanity.com/paper/2110.10183/) (2021)
- [TTS-CGAN: A Transformer Time-Series Conditional GAN for Biosignal Data Augmentation](https://mlsanity.com/paper/2206.13676/) (2022)
- [Joint Face Image Restoration and Frontalization for Recognition](https://mlsanity.com/paper/2105.09907/) (2021)
- [3D-FM GAN: Towards 3D-Controllable Face Manipulation](https://mlsanity.com/paper/2208.11257/) (2022)
- [Pose-disentangled Contrastive Learning for Self-supervised Facial Representation](https://mlsanity.com/paper/2211.13490/) (2022)
- [Layout-Guided Novel View Synthesis from a Single Indoor Panorama](https://mlsanity.com/paper/2103.17022/) (2021)
- [Generative Flows with Invertible Attentions](https://mlsanity.com/paper/2106.03959/) (2021)
- [CoPE: Conditional image generation using Polynomial Expansions](https://mlsanity.com/paper/2104.05077/) (2021)
- [SIEDOB: Semantic Image Editing by Disentangling Object and Background](https://mlsanity.com/paper/2303.13062/) (2023)
- [Deep View Synthesis via Self-Consistent Generative Network](https://mlsanity.com/paper/2101.10844/) (2021)
- [BareSkinNet: De-makeup and De-lighting via 3D Face Reconstruction](https://mlsanity.com/paper/2209.09029/) (2022)
- [Local and Global GANs with Semantic-Aware Upsampling for Image Generation](https://mlsanity.com/paper/2203.00047/) (2022)
- [PoseFace: Pose-Invariant Features and Pose-Adaptive Loss for Face Recognition](https://mlsanity.com/paper/2107.11721/) (2021)
- [A Synthesis-Based Approach for Thermal-to-Visible Face Verification](https://mlsanity.com/paper/2108.09558/) (2021)
- [DyStyle: Dynamic Neural Network for Multi-Attribute-Conditioned Style Editing](https://mlsanity.com/paper/2109.10737/) (2021)
- [A Survey on Future Frame Synthesis: Bridging Deterministic and Generative Approaches](https://mlsanity.com/paper/2401.14718/) (2024)
- [Contrastive Attention Network with Dense Field Estimation for Face Completion](https://mlsanity.com/paper/2112.10310/) (2021)
- [DisCO: Portrait Distortion Correction with Perspective-Aware 3D GANs](https://mlsanity.com/paper/2302.12253/) (2023)

## Abstract (arXiv)

Photorealistic frontal view synthesis from a single face image has a wide range of applications in the field of face recognition. Although data-driven deep learning methods have been proposed to address this problem by seeking solutions from ample face data, this problem is still challenging because it is intrinsically ill-posed. This paper proposes a Two-Pathway Generative Adversarial Network (TP-GAN) for photorealistic frontal view synthesis by simultaneously perceiving global structures and local details. Four landmark located patch networks are proposed to attend to local textures in addition to the commonly used global encoder-decoder network. Except for the novel architecture, we make this ill-posed problem well constrained by introducing a combination of adversarial loss, symmetry loss and identity preserving loss. The combined loss function leverages both frontal face distribution and pre-trained discriminative deep face models to guide an identity preserving inference of frontal views from profiles. Different from previous deep learning methods that mainly rely on intermediate features for recognition, our method directly leverages the synthesized identity preserving image for downstream tasks like face recognition and attribution estimation. Experimental results demonstrate that our method not only presents compelling perceptual results but also outperforms state-of-the-art results on large pose face recognition.