# Custom Structure Preservation in Face Aging

arXiv: https://arxiv.org/abs/2207.11025 · HTML: https://mlsanity.com/paper/2207.11025/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 48 references that are in the map.
- First posted on arXiv 22 Jul 2022.

## Builds on (most cited first, 30 of 34)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation](https://mlsanity.com/paper/2008.00951/) (2020)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [Age Progression/Regression by Conditional Adversarial Autoencoder](https://mlsanity.com/paper/1702.08423/) (2017)
- [Diverse Image-to-Image Translation via Disentangled Representations](https://mlsanity.com/paper/1808.00948/) (2018)
- [cGANs with Projection Discriminator](https://mlsanity.com/paper/1802.05637/) (2018)
- [Swapping Autoencoder for Deep Image Manipulation](https://mlsanity.com/paper/2007.00653/) (2020)
- [Full-Gradient Representation for Neural Network Visualization](https://mlsanity.com/paper/1905.00780/) (2019)
- [Eigen-CAM: Class Activation Map using Principal Components](https://mlsanity.com/paper/2008.00299/) (2020)
- [Deformable GANs for Pose-based Human Image Generation](https://mlsanity.com/paper/1801.00055/) (2017)
- [GANimation: Anatomically-aware Facial Animation from a Single Image](https://mlsanity.com/paper/1807.09251/) (2018)
- [Fader Networks: Manipulating Images by Sliding Attributes](https://mlsanity.com/paper/1706.00409/) (2017)
- [Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping](https://mlsanity.com/paper/1809.05852/) (2018)
- [Only a Matter of Style: Age Transformation Using a Style-Based Regression Model](https://mlsanity.com/paper/2102.02754/) (2021)
- [A Latent Transformer for Disentangled Face Editing in Images and Videos](https://mlsanity.com/paper/2106.11895/) (2021)
- [Lifespan Age Transformation Synthesis](https://mlsanity.com/paper/2003.09764/) (2020)

## Built on by (most cited first, 19 of 19)

- [Face Aging via Diffusion-based Editing](https://mlsanity.com/paper/2309.11321/) (2023)
- [Pluralistic Aging Diffusion Autoencoder](https://mlsanity.com/paper/2303.11086/) (2023)
- [Identity-Preserving Aging of Face Images via Latent Diffusion Models](https://mlsanity.com/paper/2307.08585/) (2023)
- [DiffAge3D: Diffusion-based 3D-aware Face Aging](https://mlsanity.com/paper/2408.15922/) (2024)
- [MyTimeMachine: Personalized Facial Age Transformation](https://mlsanity.com/paper/2411.14521/) (2024)
- [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)
- [Exploring 3D-aware Lifespan Face Aging via Disentangled Shape-Texture Representations](https://mlsanity.com/paper/2312.16881/) (2023)
- [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)
- [The Aging Multiverse: Generating Condition-Aware Facial Aging Tree via Training-Free Diffusion](https://mlsanity.com/paper/2506.21008/) (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)
- [A Race Bias Free Face Aging Model for Reliable Kinship Verification](https://mlsanity.com/paper/2509.15177/) (2025)
- [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)

In this work, we propose a novel architecture for face age editing that can produce structural modifications while maintaining relevant details present in the original image. We disentangle the style and content of the input image and propose a new decoder network that adopts a style-based strategy to combine the style and content representations of the input image while conditioning the output on the target age. We go beyond existing aging methods allowing users to adjust the degree of structure preservation in the input image during inference. To this purpose, we introduce a masking mechanism, the CUstom Structure Preservation module, that distinguishes relevant regions in the input image from those that should be discarded. CUSP requires no additional supervision. Finally, our quantitative and qualitative analysis which include a user study, show that our method outperforms prior art and demonstrates the effectiveness of our strategy regarding image editing and adjustable structure preservation. Code and pretrained models are available at https://github.com/guillermogotre/CUSP.