{"title":"深脸在野外旋转","authors":"Shohei Morikawa, S. Saito","doi":"10.1145/3281505.3281606","DOIUrl":null,"url":null,"abstract":"Generating face images in various directions from an image will be useful to create avatars in VR. In this paper, we introduce a new deep generative model to generate turnaround face images from an image via a latent code space with a parameter. The model was learned with a large scale image dataset annotated with attributes but not including exact target images.","PeriodicalId":138249,"journal":{"name":"Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep face rotation in the wild\",\"authors\":\"Shohei Morikawa, S. Saito\",\"doi\":\"10.1145/3281505.3281606\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Generating face images in various directions from an image will be useful to create avatars in VR. In this paper, we introduce a new deep generative model to generate turnaround face images from an image via a latent code space with a parameter. The model was learned with a large scale image dataset annotated with attributes but not including exact target images.\",\"PeriodicalId\":138249,\"journal\":{\"name\":\"Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology\",\"volume\":\"69 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3281505.3281606\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3281505.3281606","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Generating face images in various directions from an image will be useful to create avatars in VR. In this paper, we introduce a new deep generative model to generate turnaround face images from an image via a latent code space with a parameter. The model was learned with a large scale image dataset annotated with attributes but not including exact target images.
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