摘要翻译:
我们提出了一种方法,该方法获取小提琴或钢琴演奏的音频作为输入,并输出骨骼预测的视频,该视频进一步用于动画化身。关键的想法是创建一个化身的动画,他们的手移动类似于钢琴家或小提琴家会做什么,只是从音频。目标是一个完整详细的正确的手臂和手指运动,然而,还不清楚是否可以从音乐中预测身体运动。在本文中,我们给出了第一个结果,表明自然体动力学是完全可以预测的。我们建立了一个LSTM网络,对上传到互联网的小提琴和钢琴独奏视频进行培训。预测的点被应用到一个操纵的化身上,以创建动画。
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英文标题:
《Audio to Body Dynamics》
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作者:
Eli Shlizerman, Lucio M. Dery, Hayden Schoen, Ira
Kemelmacher-Shlizerman
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最新提交年份:
2017
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分类信息:
一级分类:Electrical Engineering and Systems Science 电气工程与系统科学
二级分类:Audio and Speech Processing 音频和语音处理
分类描述:Theory and methods for processing signals representing audio, speech, and language, and their applications. This includes analysis, synthesis, enhancement, transformation, classification and interpretation of such signals as well as the design, development, and evaluation of associated signal processing systems. Machine learning and pattern analysis applied to any of the above areas is also welcome. Specific topics of interest include: auditory modeling and hearing aids; acoustic beamforming and source localization; classification of acoustic scenes; speaker separation; active noise control and echo cancellation; enhancement; de-reverberation; bioacoustics; music signals analysis, synthesis and modification; music information retrieval; audio for multimedia and joint audio-video processing; spoken and written language modeling, segmentation, tagging, parsing, understanding, and translation; text mining; speech production, perception, and psychoacoustics; speech analysis, synthesis, and perceptual modeling and coding; robust speech recognition; speaker recognition and characterization; deep learning, online learning, and graphical models applied to speech, audio, and language signals; and implementation aspects ranging from system architecture to fast algorithms.
处理代表音频、语音和语言的信号的理论和方法及其应用。这包括分析、合成、增强、转换、分类和解释这些信号,以及相关信号处理系统的设计、开发和评估。机器学习和模式分析应用于上述任何领域也是受欢迎的。感兴趣的具体主题包括:听觉建模和助听器;声波束形成与声源定位;声场景分类;说话人分离;有源噪声控制和回声消除;增强;去混响;生物声学;音乐信号的分析、合成与修饰;音乐信息检索;多媒体音频和联合音视频处理;口语和书面语建模、切分、标注、句法分析、理解和翻译;文本挖掘;言语产生、感知和心理声学;语音分析、合成、感知建模和编码;鲁棒语音识别;说话人识别与特征描述;应用于语音、音频和语言信号的深度学习、在线学习和图形模型;以及从系统架构到快速算法的实现方面。
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一级分类:Computer Science 计算机科学
二级分类:Computer Vision and Pattern Recognition 计算机视觉与模式识别
分类描述:Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类:Computer Science 计算机科学
二级分类:Sound 声音
分类描述:Covers all aspects of computing with sound, and sound as an information channel. Includes models of sound, analysis and synthesis, audio user interfaces, sonification of data, computer music, and sound signal processing. Includes ACM Subject Class H.5.5, and intersects with H.1.2, H.5.1, H.5.2, I.2.7, I.5.4, I.6.3, J.5, K.4.2.
涵盖了声音计算的各个方面,以及声音作为一种信息通道。包括声音模型、分析和合成、音频用户界面、数据的可听化、计算机音乐和声音信号处理。包括ACM学科类H.5.5,并与H.1.2、H.5.1、H.5.2、I.2.7、I.5.4、I.6.3、J.5、K.4.2交叉。
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英文摘要:
We present a method that gets as input an audio of violin or piano playing, and outputs a video of skeleton predictions which are further used to animate an avatar. The key idea is to create an animation of an avatar that moves their hands similarly to how a pianist or violinist would do, just from audio. Aiming for a fully detailed correct arms and fingers motion is a goal, however, it's not clear if body movement can be predicted from music at all. In this paper, we present the first result that shows that natural body dynamics can be predicted at all. We built an LSTM network that is trained on violin and piano recital videos uploaded to the Internet. The predicted points are applied onto a rigged avatar to create the animation.
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PDF链接:
https://arxiv.org/pdf/1712.09382