AI-Based Music to Dance Synthesis and Rendering

AI Choreographer is a deep learning model that is able to generate dance motions according to music easily. However, several difficulties and weaknesses in the model still make it difficult to use. For example, the model generates realistic motions, but sometimes the motions are repetitive or do not respond to the audio correctly. Also, the model does not have a usable render that allows it to directly animate the provided models with generated data. We improved our base model to generate more realistic and better dance motions, and we also created a usable automated render pipeline to directly render the generated motions into an animation of the human models provided by the user. We improved the generation quality by introducing more audio features into the model so that the models can utilize more features for better results. Also, we overcame different difficulties in the rendering process, including applying the AI-generated numpy motion data to provided SMPL models and converting the animated SMPL models into usable FBX models. In general, the improved model generates motions that are more diverse and realistic than the base model, which provides dance motions that have higher quality.

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Publication Details

Journal
Applied and Computational Engineering
Published
2024-12-12
DOI
https://doi.org/10.54254/2755-2721/2024.18318
Primary Topic
Human Motion and Animation
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-Based Music to Dance Synthesis and Rendering

Bohan Jin
Applied and Computational Engineering
Human Motion and Animation
article

AI-Based Music to Dance Synthesis and Rendering

Bohan Jin
article en

Abstract

AI Choreographer is a deep learning model that is able to generate dance motions according to music easily. However, several difficulties and weaknesses in the model still make it difficult to use. For example, the model generates realistic motions, but sometimes the motions are repetitive or do not respond to the audio correctly. Also, the model does not have a usable render that allows it to directly animate the provided models with generated data. We improved our base model to generate more realistic and better dance motions, and we also created a usable automated render pipeline to directly render the generated motions into an animation of the human models provided by the user. We improved the generation quality by introducing more audio features into the model so that the models can utilize more features for better results. Also, we overcame different difficulties in the rendering process, including applying the AI-generated numpy motion data to provided SMPL models and converting the animated SMPL models into usable FBX models. In general, the improved model generates motions that are more diverse and realistic than the base model, which provides dance motions that have higher quality.

Applied and Computational EngineeringVol. 114(1)
Bancroft School of Massage Therapy (US)
Openalex Percentile: Top 73%
Human Motion and Animation
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AI-Based Music to Dance Synthesis and Rendering — Bohan Jin · Applied and Computational Engineering (2024) | TGRS Research Map | TGRS