  Human shape and clothing estimation has gained significant prominence in
various domains, including online shopping, fashion retail, augmented reality
(AR), virtual reality (VR), and gaming. The visual representation of human
shape and clothing has become a focal point for computer vision researchers in
recent years. This paper presents a comprehensive survey of the major works in
the field, focusing on four key aspects: human shape estimation, fashion
generation, landmark detection, and attribute recognition. For each of these
tasks, the survey paper examines recent advancements, discusses their strengths
and limitations, and qualitative differences in approaches and outcomes. By
exploring the latest developments in human shape and clothing estimation, this
survey aims to provide a comprehensive understanding of the field and inspire
future research in this rapidly evolving domain.
