Abstract
As a form of artistic expression, dance accompanied by music enriches the cultural life of human beings and stimulates the creative enthusiasm of the public. Choreography is usually done by professional choreographers. It is highly professional and time-consuming. The development of technology is changing the way of artistic creation. The development of motion capture technology and artificial intelligence makes computer-based automatic choreography possible. This paper proposes a method of music choreography based on deep learning. First, we use Kinect to extract and filter actions and get actions with high authenticity and continuity. Then, based on the constant Q transformation, the overall note density and beats per minute (BPM) of the target music are extracted, and preliminary matching is performed with features such as action speed and spatiality, and then, the local features of the music and action segments based on rhythm and intensity are matched. The experimental results show that the method proposed in this paper can effectively synthesize dance movements. The speed and other characteristics of each movement segment in the synthesis result are very uniform, and the overall choreography is more aesthetic.
Highlights
Movement is the soul of dance. e first problem that needs to be solved in computer choreography is how to digitize dance movements
The research on dance movement synthesis is mainly based on motion capture data
(3) UCY dataset (University of Cyprus), containing a total of 161 sequences 147509 frames, providing dance moves in Greek, Cypriot dance, and other styles, but only 8 of them are relatively complete movements accompanied by music, a total of 28892 frames, and the rest of the sequences are single movement fragments with a short time, which is not conducive to complete dance [14]. (4) e CMU dataset contains a total of 2235 sequences of 98,7341 frames. is is the largest motion capture dataset published so far, covering a wide range of motion types, of which only 64,300 frames are for pure dance movements, with no accompanying music
Summary
Movement is the soul of dance. e first problem that needs to be solved in computer choreography is how to digitize dance movements. E first problem that needs to be solved in computer choreography is how to digitize dance movements. In the display of dance, the traditional method is that the choreographer verbally explains or tells the performer by drawing a picture, and the dancer shows the specific movement, and subsequent modifications need to be carried out on the actual effect. Many existing motion generation technologies based on machine learning have been applied to dance research, including dimension reduction technology [1], Gaussian process [2], and hidden Markov model [3], so as to capture the potential correlation between music and dance motion characteristics. Gaussian process late variable models can effectively summarize the changes of human cloud force, but they are not suitable for real-time generation because they require a lot of computing and memory resources [5]. In order to solve the defects of computer choreography based on machine learning, this paper will introduce deep learning method to improve the novelty and coherence of generated actions
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