@inproceedings{ca935cfc70764a988d18fc9c8e296b64,
title = "COMPONENT-BASED TRANSFORMATION FOR PERSON IMAGE GENERATION",
abstract = "Person image generation has attracted more and more attention because it has many applications. This paper focuses on pose transfer and virtual try-on. We propose a flow-based method called attribute-decomposited spatial transformation network. It warps different body parts separately using segmentation and then fuses different body parts and generates images. The flow-based technique enables the model to generate high-quality person images for pose transfer and the attribute-decomposited technique enables the model to support virtual try-on. The proposed method was evaluated on Deepfashion dataset. Quantitative and qualitative experimental results show the superiority of the method.",
keywords = "Person image synthesis, pose-transfer, virtual try-on",
author = "Tsai, {Wen Jiin} and Chen, {Po Hsiang}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 29th IEEE International Conference on Image Processing, ICIP 2022 ; Conference date: 16-10-2022 Through 19-10-2022",
year = "2022",
doi = "10.1109/ICIP46576.2022.9897683",
language = "English",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "4108--4112",
booktitle = "2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings",
address = "美國",
}