A Music-Driven Deep Generative Adversarial Model for Guzheng Playing Animation

Area of Science:

• Computer Graphics
• Artificial Intelligence
• Music Technology

Background:

• Automatic generation of musical instrument playing animations is underexplored.
• Challenges include the complex temporal relationship between music and human motion, and limited high-quality datasets.

Purpose of the Study:

• To propose a fully automatic, deep learning-based framework for synthesizing realistic upper body animations for guzheng playing.
• To capture the intricate temporal dynamics between guzheng music and human motion.

Main Methods:

• A generative adversarial network (GAN) based approach was designed using an audiovisual motion capture dataset.
• Data augmentation techniques were employed to enhance generalization across diverse guzheng music inputs.

Main Results:

• The framework successfully generates visually plausible guzheng-playing animations synchronized with novel music.
• Objective and subjective experiments demonstrate superior performance compared to state-of-the-art methods.
• An ablation study validated the effectiveness of individual framework components.

Conclusions:

• The proposed deep learning framework offers a robust solution for automatic guzheng animation synthesis.
• The method achieves high synchronization and realism, advancing the field of music-driven animation.