What's in a Decade? Transforming Faces Through Time

TL;DR

This paper introduces a new dataset and a generative framework to resynthesize and visualize how individual portraits would appear across different decades, capturing subtle temporal style changes while preserving identity.

Contribution

The work presents the Faces Through Time dataset and a novel multi-decade generative model for temporal portrait resynthesis, outperforming existing image translation methods.

Findings

Our method more accurately resynthesizes portraits across decades.

The framework captures subtle style changes like hairstyles and makeup.

It maintains the identity of the input portrait effectively.

Abstract

How can one visually characterize people in a decade? In this work, we assemble the Faces Through Time dataset, which contains over a thousand portrait images from each decade, spanning the 1880s to the present day. Using our new dataset, we present a framework for resynthesizing portrait images across time, imagining how a portrait taken during a particular decade might have looked like, had it been taken in other decades. Our framework optimizes a family of per-decade generators that reveal subtle changes that differentiate decade--such as different hairstyles or makeup--while maintaining the identity of the input portrait. Experiments show that our method is more effective in resynthesizing portraits across time compared to state-of-the-art image-to-image translation methods, as well as attribute-based and language-guided portrait editing models. Our code and data will be available at https://facesthroughtime.github.io