A system for automatic animation of piano performances

Playing the piano requires one to precisely position one's hand in order to strike particular combinations of keys at specific moments in time. This paper presents the first system for automatically generating three‐dimensional animations of piano performance, given an input midi music file. A graph theory‐based motion planning method is used to decide which set of fingers should strike the piano keys for each chord. As the progression of the music is anticipated, the positions of unused fingers are calculated to make possible efficient fingering of future notes. Initial key poses of the hands, including those for complex piano techniques such as crossovers and arpeggio, are determined on the basis of the finger positions and piano theory. An optimization method is used to refine these poses, producing a natural and minimal energy pose sequence. Motion transitions between poses are generated using a combination of sampled piano playing motion and music features, allowing the system to support different playing styles. Our approach is validated through direct comparison with actual piano playing and simulation of a complete music piece requiring various playing skills. Extensions of our system are discussed. Copyright © 2012 John Wiley & Sons, Ltd.

[1]  Samir I. Sayegh,et al.  Fingering for string instruments with the optimum path paradigm , 1989 .

[2]  Vincenzo Lombardo,et al.  Guitar Fingering for Music Performance , 2005, ICMC.

[3]  Peter Desain,et al.  An Ergonomic Model of Keyboard Fingering for Melodic Fragments Sibelius Academy of Music, Helsinski , 2022 .

[4]  Karl H.E. Kroemer,et al.  A Finger Model with Constant Tendon Moment Arms , 1993 .

[5]  Kunihiro Chihara,et al.  Three-dimensional modeling of the human hand with motion constraints , 1999, Image Vis. Comput..

[6]  Daniel R. Tuohy Creating Tablature and Arranging Music for Guitar with Genetic Algorithms and Artificial Neural Networks Arranging Music for Guitar with Genetic Algorithms and Artificial Neural Networks Creating Tablature and Arranging Music for Guitar with Genetic Algorithms and Artificial Neural Networks , 2006 .

[7]  M H Schieber,et al.  Quantifying the Independence of Human Finger Movements: Comparisons of Digits, Hands, and Movement Frequencies , 2000, The Journal of Neuroscience.

[8]  Nadia Magnenat-Thalmann,et al.  Neural network-based violinist's hand animation , 2000, Proceedings Computer Graphics International 2000.

[9]  Peter F. Driessen,et al.  Path Difference Learning for Guitar Fingering Problem , 2004, ICMC.

[10]  Hank Heijink,et al.  On the Complexity of Classical Guitar Playing: Functional Adaptations to Task Constraints , 2002, Journal of motor behavior.

[11]  Alexandre Bezerra Viana Technological Improvements in the SIEDP , 2003 .

[12]  Hirokazu Kameoka,et al.  Automatic Decision of Piano Fingering Based on a Hidden Markov Models , 2007, IJCAI.

[13]  Vincenzo Lombardo,et al.  A segmentation-based prototype to compute string instruments fingering , 2004 .

[14]  Damon Shing-Min Liu,et al.  An intelligent virtual piano tutor , 2006, VRCIA '06.

[15]  Walter D. Potter,et al.  A Genetic Algorithm for the Automatic Generation of Playable Guitar Tablature , 2005, ICMC.

[16]  Victor B. Zordan,et al.  Physically based grasping control from example , 2005, SCA '05.

[17]  Helmut Wicht Bilder einer Ausstellung , 2014 .

[18]  Karan Singh,et al.  Eurographics/siggraph Symposium on Computer Animation (2003) Handrix: Animating the Human Hand , 2003 .