Fashion Meets Computer Vision: A Survey

TL;DR

This survey comprehensively reviews over 200 computer vision techniques applied to fashion, covering detection, analysis, synthesis, and recommendation, and discusses datasets, evaluation protocols, and future research directions.

Contribution

It provides the first extensive overview of fashion-related computer vision research, summarizing key tasks, datasets, and evaluation methods, and identifies promising future directions.

Findings

Summarizes 200+ fashion-related computer vision works

Details benchmark datasets and evaluation protocols

Highlights future research directions in fashion vision

Abstract

Fashion is the way we present ourselves to the world and has become one of the world's largest industries. Fashion, mainly conveyed by vision, has thus attracted much attention from computer vision researchers in recent years. Given the rapid development, this paper provides a comprehensive survey of more than 200 major fashion-related works covering four main aspects for enabling intelligent fashion: (1) Fashion detection includes landmark detection, fashion parsing, and item retrieval, (2) Fashion analysis contains attribute recognition, style learning, and popularity prediction, (3) Fashion synthesis involves style transfer, pose transformation, and physical simulation, and (4) Fashion recommendation comprises fashion compatibility, outfit matching, and hairstyle suggestion. For each task, the benchmark datasets and the evaluation protocols are summarized. Furthermore, we highlight promising directions for future research.