{"title":"A Method to Generate Posed Person Image with few Context Images","authors":"H. Nakada, H. Asoh","doi":"10.1109/imcom53663.2022.9721635","DOIUrl":null,"url":null,"abstract":"We report a method to generate an image of an arbitrary person in an arbitrary pose, where the person’s characteristics are provided as a few sample images. The rapid development in image generation techniques using deep neural networks makes it possible to generate photo-realistic images. However, controlling the content of the generated images, such as a person’s clothing or pose, is still an open problem. The method we propose creates a pose invariant person representation using the attention mechanism and generates posed images by applying pose query on the representation. We evaluated the method using 3D rendered synthetic data and confirmed that the created person representation is pose-invariant, and we can render good quality images with the representation.","PeriodicalId":367038,"journal":{"name":"2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/imcom53663.2022.9721635","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
We report a method to generate an image of an arbitrary person in an arbitrary pose, where the person’s characteristics are provided as a few sample images. The rapid development in image generation techniques using deep neural networks makes it possible to generate photo-realistic images. However, controlling the content of the generated images, such as a person’s clothing or pose, is still an open problem. The method we propose creates a pose invariant person representation using the attention mechanism and generates posed images by applying pose query on the representation. We evaluated the method using 3D rendered synthetic data and confirmed that the created person representation is pose-invariant, and we can render good quality images with the representation.