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Interactive dance performance evaluation using timing and accuracy similarity 互动式舞蹈表演评价采用时间和精度相似性
ACM SIGGRAPH 2018 Posters Pub Date : 2018-08-12 DOI: 10.1145/3230744.3230798
Yeonho Kim, Daijin Kim
{"title":"Interactive dance performance evaluation using timing and accuracy similarity","authors":"Yeonho Kim, Daijin Kim","doi":"10.1145/3230744.3230798","DOIUrl":"https://doi.org/10.1145/3230744.3230798","url":null,"abstract":"This paper presents a dance performance evaluation how well a learner mimics the teacher's dance as follows. We estimate the human skeletons, then extract dance features such as torso and first and second-degree feature, and compute the similarity score between the teacher and the learner dance sequence in terms of timing and pose accuracies. To validate the proposed dance evaluation method, we conducted several experiments on a large K-Pop dance database. The proposed methods achieved 98% concordance with experts' evaluation on dance performance.","PeriodicalId":226759,"journal":{"name":"ACM SIGGRAPH 2018 Posters","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121721874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
RSGAN: face swapping and editing using face and hair representation in latent spaces RSGAN:利用潜在空间中的人脸和毛发表示进行人脸交换和编辑
ACM SIGGRAPH 2018 Posters Pub Date : 2018-04-10 DOI: 10.1145/3230744.3230818
Ryota Natsume, Tatsuya Yatagawa, S. Morishima
{"title":"RSGAN: face swapping and editing using face and hair representation in latent spaces","authors":"Ryota Natsume, Tatsuya Yatagawa, S. Morishima","doi":"10.1145/3230744.3230818","DOIUrl":"https://doi.org/10.1145/3230744.3230818","url":null,"abstract":"This abstract introduces a generative neural network for face swapping and editing face images. We refer to this network as \"region-separative generative adversarial network (RSGAN)\". In existing deep generative models such as Variational autoencoder (VAE) and Generative adversarial network (GAN), training data must represent what the generative models synthesize. For example, image inpainting is achieved by training images with and without holes. However, it is difficult or even impossible to prepare a dataset which includes face images both before and after face swapping because faces of real people cannot be swapped without surgical operations. We tackle this problem by training the network so that it synthesizes synthesize a natural face image from an arbitrary pair of face and hair appearances. In addition to face swapping, the proposed network can be applied to other editing applications, such as visual attribute editing and random face parts synthesis.","PeriodicalId":226759,"journal":{"name":"ACM SIGGRAPH 2018 Posters","volume":"54 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116762252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 128
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