Abstract
Musical choreography is usually completed by professional choreographers, which is very professional and time-consuming. In order to realize the intelligent choreography of musical, based on the mixed density network (MDN), this paper generates the dance matching with the target music through three steps: motion generation, motion screening, and feature matching. The choreography results in this paper have a high degree of matching with music, which makes it possible for the development of motion capture technology and artificial intelligence and computer automatic choreography based on music. In the process of motion generation, the average value of Gaussian model output by MDN is used as the bone position and the consistency of motion is measured according to the change rate of joint velocity in adjacent frames in the process of motion selection. Compared with the existing studies, the dance generated in this paper has improved in motion coherence and realism. In this paper, a multilevel music and action feature matching algorithm combining global feature matching and local feature matching is proposed. The algorithm improves the unity and coherence of music and action. The algorithm proposed in this paper improves the consistency and novelty of movement, the compatibility with music, and the controllability of dance characteristics. Therefore, the algorithm in this paper technically changes the way of artistic creation and provides the possibility for the development of motion capture technology and artificial intelligence.
Highlights
As a performing art form, dance is generally based on rhythmic movements to music
For the dance movements of virtual characters, especially the dance animations created manually by users, the animator needs to manually adjust the position and rotation of each bone of the model in key frames. Completing this task is very time-consuming and requires the animator to be experienced, which greatly limits the development of virtual character dance animation. erefore, a successful dance synthesis algorithm can be useful in areas such as music-assisted dance teaching [5,6,7], audio-visual game character movement generation [8, 9], human behavior research [10,11,12,13], and virtual reality
Computational Intelligence and Neuroscience part introduces the research methods and research results in this field, as well as the research content and innovation of this paper. e third part introduces the model structure of the action generation model used in this paper, as well as the parameter selection in the process of model training and prediction. e fourth part puts forward the multi-level feature matching algorithm of music and action and arranges the dance actions generated in the third chapter according to the characteristics of the target music. e fifth part verifies the effectiveness of the musical intelligent choreography scheme based on mixed density network (MDN). e sixth chapter summarizes the current work and research results
Summary
As a performing art form, dance is generally based on rhythmic movements to music. Choreography for musicals is usually done by talented professionals, which is challenging and time-consuming. For the dance movements of virtual characters, especially the dance animations created manually by users, the animator needs to manually adjust the position and rotation of each bone of the model in key frames Completing this task is very time-consuming and requires the animator to be experienced, which greatly limits the development of virtual character dance animation. Erefore, a successful dance synthesis algorithm can be useful in areas such as music-assisted dance teaching [5,6,7], audio-visual game character movement generation [8, 9], human behavior research [10,11,12,13], and virtual reality. Computational Intelligence and Neuroscience part introduces the research methods and research results in this field, as well as the research content and innovation of this paper. e third part introduces the model structure of the action generation model used in this paper, as well as the parameter selection in the process of model training and prediction. e fourth part puts forward the multi-level feature matching algorithm of music and action and arranges the dance actions generated in the third chapter according to the characteristics of the target music. e fifth part verifies the effectiveness of the musical intelligent choreography scheme based on MDN. e sixth chapter summarizes the current work and research results
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