Chen, Eric MingSun, JinKhandelwal, ApoorvLischinski, DaniSnavely, NoahAverbuch-Elor, HadarMyszkowski, KarolNiessner, Matthias2023-05-032023-05-0320231467-8659https://doi.org/10.1111/cgf.14761https://diglib.eg.org:443/handle/10.1111/cgf14761How can one visually characterize photographs of people over time? In this work, we describe the Faces Through Time dataset, which contains over a thousand portrait images per decade from the 1880s to the present day. Using our new dataset, we devise 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 decades-such as different hairstyles or makeup-while maintaining the identity of the input portrait. Experiments show that our method can more effectively resynthesizing portraits across time compared to state-of-theart image-to-image translation methods, as well as attribute-based and language-guided portrait editing models. Our code and data will be available at facesthroughtime.github.io.CCS Concepts: Computing methodologies -> Image manipulation; Computer graphics; Computer visionComputing methodologiesImage manipulationComputer graphicsComputer vision10.1111/cgf.14761281-29111 pages