Morphing Architectures for Pose-based Image Generation of People in Clothing

Abstract

This project investigates the task of conditional image generation from misaligned sources, with an example application in the context of content creation for the fashion industry.
The problem of spatial misalignment between images is identified, the related literature is discussed, and different approaches are introduced to address it. In particular, several non-linear differentiable morphing modules are designed and integrated in current architectures for image-to-image translation.
The proposed method for conditional image generation is applied on a clothes swapping task, using a real-world dataset of fashion images provided by Zalando. In comparison to previous methods for clothes swapping and virtual try-on, the result achieved with our method are of high visual quality and achieve precise reconstruction of the details of the garments.

Federico Baldassarre

PhD Student in Deep Learning

My research focuses on explainability and reasoning in Deep Learning.