# Dual-reference Age Synthesis

## TL;DR

This paper introduces a dual-reference age synthesis framework that generates age-progressed or regressed images by using two input images, one for identity and one for age, enabling more flexible and accurate age synthesis.

## Contribution

The paper proposes a novel dual-reference framework for age synthesis that uses two images instead of a fixed age number, improving flexibility and control in age transformation.

## Key findings

- Effective on UTKFace and CACD datasets
- Outperforms traditional single-reference methods
- Demonstrates high flexibility and realistic results

## Abstract

Age synthesis methods typically take a single image as input and use a specific number to control the age of the generated image. In this paper, we propose a novel framework taking two images as inputs, named dual-reference age synthesis (DRAS), which approaches the task differently; instead of using "hard" age information, i.e. a fixed number, our model determines the target age in a "soft" way, by employing a second reference image. Specifically, the proposed framework consists of an identity agent, an age agent and a generative adversarial network. It takes two images as input - an identity reference and an age reference - and outputs a new image that shares corresponding features with each. Experimental results on two benchmark datasets (UTKFace and CACD) demonstrate the appealing performance and flexibility of the proposed framework.