# AgingMapGAN (AMGAN): High-Resolution Controllable Face Aging with Spatially-Aware Conditional GANs

arXiv: https://arxiv.org/abs/2008.10960 · HTML: https://mlsanity.com/paper/2008.10960/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and A New Benchmark](https://mlsanity.com/paper/2210.09835/) (2022)
- [MyTimeMachine: Personalized Facial Age Transformation](https://mlsanity.com/paper/2411.14521/) (2024)
- [Diverse and Lifespan Facial Age Transformation Synthesis with Identity Variation Rationality Metric](https://mlsanity.com/paper/2401.14036/) (2024)
- [Locally Controlled Face Aging with Latent Diffusion Models](https://mlsanity.com/paper/2507.21600/) (2025)

## Abstract (arXiv)

Existing approaches and datasets for face aging produce results skewed towards the mean, with individual variations and expression wrinkles often invisible or overlooked in favor of global patterns such as the fattening of the face. Moreover, they offer little to no control over the way the faces are aged and can difficultly be scaled to large images, thus preventing their usage in many real-world applications. To address these limitations, we present an approach to change the appearance of a high-resolution image using ethnicity-specific aging information and weak spatial supervision to guide the aging process. We demonstrate the advantage of our proposed method in terms of quality, control, and how it can be used on high-definition images while limiting the computational overhead.