Shoichi Furukawa，Takuya Kato，Pavel Savkin，Shigeo Morishima, “Video Reshuffling: Automatic Video Dubbing without Prior Knowledge”, ACM SIGGRAPH 2016, Posters, Anaheim, USA, 2016.7/24-7/28 워드 스펀지.

Abstract

asp.net gridview 엑셀 다운로드. However, it is very difficult to achieve the visual-audio synchronization. That is to say in general a new audio does not synchronize with actor\u2019s mouth motion 좀보이드. This discrepancy can disturb comprehension of video contents. Therefore many methods have been researched so far to solve this problem. [Thies et al. 2016] proposed a method which can reenact the video while maintaining source actor\u2019s visual-audio synchronization by using 3D facial statistical models Download Pacific Rim movies. However their method cannot be applied to videos in which faces cannot be 3D-reconstructed, for example, vintage videos, 2D animations and more. On the other hand, image-based methods can be applied to a variety of videos Free download to PowerPoint 2013. [Ezzat et al. 2002] proposed an image-based method to generate a speech animation. However, as phonemes correspond to mouth images in one-to-one in their model, they cannot consider coarticulation Download me and you from the beginning. [Bregler et al. 1997] proposed an alternative image-based approach by reusing frames in which mouth motion synchronizes with new audio. This approach can achieve coarticulation, but phonemematching tables are required when applying to dubbing videos Beast Almighty. In this paper, we propose an image-based method to automatically generate a variety of dubbing videos with visual-audio synchronization by frame-reusing without phoneme information Heartgold. Contribution of our method is as follows. Our method 1) can automatically generate dubbing videos with visual-audio synchronization without any prior knowledges(e.g 로보카폴리 시즌4 다운로드. phoneme information, 3D face models, generative models etc.) 2) can express coarticulation and 3) can be applied to a variety of videos such as mentioned above.\""}" data-sheets-userformat="{"2":769,"3":{"1":0},"11":3,"12":0}">Numerous video have been translated using ”dubbing,” spurred by the recent growth of video market Bucks apk. However, it is very difficult to achieve the visual-audio synchronization. That is to say in general a new audio does not synchronize with actor’s mouth motion. This discrepancy can disturb comprehension of video contents. Therefore many methods have been researched so far to solve this problem. [Thies et al. 2016] proposed a method which can reenact the video while maintaining source actor’s visual-audio synchronization by using 3D facial statistical models. However, their method cannot be applied to videos in which faces cannot be 3D-reconstructed, for example, vintage videos, 2D animations and more. On the other hand, image-based methods can be applied to a variety of videos. [Ezzat et al. 2002] proposed an image-based method to generate a speech animation. However, as phonemes correspond to mouth images in one-to-one in their model, they cannot consider coarticulation. [Bregler et al. 1997] proposed an alternative image-based approach by reusing frames in which mouth motion synchronizes with new audio. This approach can achieve coarticulation, but phonemematching tables are required when applying to dubbing videos. In this paper, we propose an image-based method to automatically generate a variety of dubbing videos with visual-audio synchronization by frame-reusing without phoneme information. Contribution of our method is as follows. Our method 1) can automatically generate dubbing videos with visual-audio synchronization without any prior knowledges (e.g. phoneme information, 3D face models, generative models etc.) 2) can express coarticulation and 3) can be applied to a variety of videos such as mentioned above.