# Graph-based Generative Face Anonymisation with Pose Preservation

arXiv: https://arxiv.org/abs/2112.05496 · HTML: https://mlsanity.com/paper/2112.05496/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 31 references that are in the map.
- First posted on arXiv 10 Dec 2021.

## Builds on (most cited first, 20 of 20)

- [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)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [Everybody Dance Now](https://mlsanity.com/paper/1808.07371/) (2018)
- [FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping](https://mlsanity.com/paper/1912.13457/) (2019)
- [Deformable GANs for Pose-based Human Image Generation](https://mlsanity.com/paper/1801.00055/) (2017)
- [Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis](https://mlsanity.com/paper/1909.12224/) (2019)
- [Progressive Pose Attention Transfer for Person Image Generation](https://mlsanity.com/paper/1904.03349/) (2019)
- [A Variational U-Net for Conditional Appearance and Shape Generation](https://mlsanity.com/paper/1804.04694/) (2018)
- [DeepPrivacy: A Generative Adversarial Network for Face Anonymization](https://mlsanity.com/paper/1909.04538/) (2019)
- [Synthesizing Images of Humans in Unseen Poses](https://mlsanity.com/paper/1804.07739/) (2018)
- [CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks](https://mlsanity.com/paper/2005.09544/) (2020)
- [Natural and Effective Obfuscation by Head Inpainting](https://mlsanity.com/paper/1711.09001/) (2017)
- [Live Face De-Identification in Video](https://mlsanity.com/paper/1911.08348/) (2019)
- [A Hybrid Model for Identity Obfuscation by Face Replacement](https://mlsanity.com/paper/1804.04779/) (2018)
- [Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation](https://mlsanity.com/paper/1908.00999/) (2019)
- [Bipartite Graph Reasoning GANs for Person Image Generation](https://mlsanity.com/paper/2008.04381/) (2020)
- [Guided Image-to-Image Translation with Bi-Directional Feature Transformation](https://mlsanity.com/paper/1910.11328/) (2019)
- [PCGAN: Partition-Controlled Human Image Generation](https://mlsanity.com/paper/1811.09928/) (2018)

## Built on by (most cited first, 9 of 9)

- [Attribute-preserving Face Dataset Anonymization via Latent Code Optimization](https://mlsanity.com/paper/2303.11296/) (2023)
- [Face Anonymization Made Simple](https://mlsanity.com/paper/2411.00762/) (2024)
- [Face De-identification: State-of-the-art Methods and Comparative Studies](https://mlsanity.com/paper/2411.09863/) (2024)
- [Facial Expression Translation using Landmark Guided GANs](https://mlsanity.com/paper/2209.02136/) (2022)
- [NullFace: Training-Free Localized Face Anonymization](https://mlsanity.com/paper/2503.08478/) (2025)
- [Reverse Personalization](https://mlsanity.com/paper/2512.22984/) (2025)
- [VerA: Versatile Anonymization Applicable to Clinical Facial Photographs](https://mlsanity.com/paper/2312.02124/) (2023)
- [Face anonymization preserving facial expressions and photometric realism](https://mlsanity.com/paper/2603.17567/) (2026)
- [HYDRO: Towards Non-Reversible Face De-Identification Using a High-Fidelity Hybrid Diffusion and Target-Oriented Approach](https://mlsanity.com/paper/2609.27011/) (2026)

## Abstract (arXiv)

We propose AnonyGAN, a GAN-based solution for face anonymisation which replaces the visual information corresponding to a source identity with a condition identity provided as any single image. With the goal to maintain the geometric attributes of the source face, i.e., the facial pose and expression, and to promote more natural face generation, we propose to exploit a Bipartite Graph to explicitly model the relations between the facial landmarks of the source identity and the ones of the condition identity through a deep model. We further propose a landmark attention model to relax the manual selection of facial landmarks, allowing the network to weight the landmarks for the best visual naturalness and pose preservation. Finally, to facilitate the appearance learning, we propose a hybrid training strategy to address the challenge caused by the lack of direct pixel-level supervision. We evaluate our method and its variants on two public datasets, CelebA and LFW, in terms of visual naturalness, facial pose preservation and of its impacts on face detection and re-identification. We prove that AnonyGAN significantly outperforms the state-of-the-art methods in terms of visual naturalness, face detection and pose preservation.