Virtual Try-On Methods: A Comprehensive Research and Analysis
- DOI
- 10.2991/978-94-6463-300-9_35How to use a DOI?
- Keywords
- Virtual try-on; deep learning; 3D; 2D; diffusion model
- Abstract
Image-based virtual try-on, as a challenging and practical real-world task, is one of the interesting research topics in recent years. Virtual try-on will become a common way to buy fashion products in the future, however, with the development in recent years, relevant review articles are not sufficient. Therefore, this paper aims to systematically introduce the development trend and current advanced algorithms in the field of virtual try-on. This paper briefly introduces various research directions in this field at present, and gives a certain degree of analysis, including 3D virtual try-on and 2D virtual try-on. Among them, this paper uses more space to describe 2D virtual try-on as it’s the mainstream direction. In conclusion, this article culminates with a comprehensive summary and projection of the complete text. The intent is to elucidate the contemporary advancements in this domain and provide a more comprehensive depiction of the potential future trajectory of development.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Haoxuan Sun PY - 2023 DA - 2023/11/27 TI - Virtual Try-On Methods: A Comprehensive Research and Analysis BT - Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023) PB - Atlantis Press SP - 339 EP - 346 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-300-9_35 DO - 10.2991/978-94-6463-300-9_35 ID - Sun2023 ER -