# High Resolution Face Age Editing

arXiv: https://arxiv.org/abs/2005.04410 · HTML: https://mlsanity.com/paper/2005.04410/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2020.

## Built on by (most cited first, 23 of 23)

- [Only a Matter of Style: Age Transformation Using a Style-Based Regression Model](https://mlsanity.com/paper/2102.02754/) (2021)
- [GAN-Control: Explicitly Controllable GANs](https://mlsanity.com/paper/2101.02477/) (2021)
- [Face Aging via Diffusion-based Editing](https://mlsanity.com/paper/2309.11321/) (2023)
- [Custom Structure Preservation in Face Aging](https://mlsanity.com/paper/2207.11025/) (2022)
- [Facial Attribute Transformers for Precise and Robust Makeup Transfer](https://mlsanity.com/paper/2104.02894/) (2021)
- [Identity-Preserving Aging of Face Images via Latent Diffusion Models](https://mlsanity.com/paper/2307.08585/) (2023)
- [Continuous Face Aging via Self-estimated Residual Age Embedding](https://mlsanity.com/paper/2105.00020/) (2021)
- [A comprehensive survey on semantic facial attribute editing using generative adversarial networks](https://mlsanity.com/paper/2205.10587/) (2022)
- [Video Face Re-Aging: Toward Temporally Consistent Face Re-Aging](https://mlsanity.com/paper/2311.11642/) (2023)
- [Diverse and Lifespan Facial Age Transformation Synthesis with Identity Variation Rationality Metric](https://mlsanity.com/paper/2401.14036/) (2024)
- [Synthetic Face Ageing: Evaluation, Analysis and Facilitation of Age-Robust Facial Recognition Algorithms](https://mlsanity.com/paper/2406.06932/) (2024)
- [From Cradle to Cane: A Two-Pass Framework for High-Fidelity Lifespan Face Aging](https://mlsanity.com/paper/2506.20977/) (2025)
- [AgeBooth: Controllable Facial Aging and Rejuvenation via Diffusion Models](https://mlsanity.com/paper/2510.05715/) (2025)
- [ToonAging: Face Re-Aging upon Artistic Portrait Style Transfer](https://mlsanity.com/paper/2402.02733/) (2024)
- [SelfAge: Personalized Facial Age Transformation Using Self-reference Images](https://mlsanity.com/paper/2502.13987/) (2025)
- [TimeMachine: Fine-Grained Facial Age Editing with Identity Preservation](https://mlsanity.com/paper/2508.11284/) (2025)
- [Face Time Traveller : Travel Through Ages Without Losing Identity](https://mlsanity.com/paper/2602.22819/) (2026)
- [Emotion Transfer Using Vector-Valued Infinite Task Learning](https://mlsanity.com/paper/2102.05075/) (2021)
- [OrthoGAN:High-Precision Image Generation for Teeth Orthodontic Visualization](https://mlsanity.com/paper/2212.14162/) (2022)
- [MeInTime: Bridging Age Gap in Identity-Preserving Face Restoration](https://mlsanity.com/paper/2603.18645/) (2026)
- [TimeWeaver: Age-Consistent Reference-Based Face Restoration with Identity Preservation](https://mlsanity.com/paper/2603.22701/) (2026)
- [DiverAge: Reliable Pluralistic Face Aging with Cross-Age Identity Relation Guidance](https://mlsanity.com/paper/2606.04881/) (2026)
- [ReAge3D: Re-Aging 3D Faces with View Consistency](https://mlsanity.com/paper/2606.18156/) (2026)

## Abstract (arXiv)

Face age editing has become a crucial task in film post-production, and is also becoming popular for general purpose photography. Recently, adversarial training has produced some of the most visually impressive results for image manipulation, including the face aging/de-aging task. In spite of considerable progress, current methods often present visual artifacts and can only deal with low-resolution images. In order to achieve aging/de-aging with the high quality and robustness necessary for wider use, these problems need to be addressed. This is the goal of the present work. We present an encoder-decoder architecture for face age editing. The core idea of our network is to create both a latent space containing the face identity, and a feature modulation layer corresponding to the age of the individual. We then combine these two elements to produce an output image of the person with a desired target age. Our architecture is greatly simplified with respect to other approaches, and allows for continuous age editing on high resolution images in a single unified model.