Design and Development of Automatic Generation of Dance Moves Using Ensemble Networks

The rapid development of artificial intelligence has made it possible to generate dance movements automatically through combining deep learning with multimodal learning to learn the projection of music features into human-like coherent motion sequences. Nevertheless, current systems suffer from the following challenges: being poorly synchronized with rhythm, not being naturally smooth, having limited adaptability to different dance genres, and having lower generalization when tested on unseen audio patterns. Legacy methods such as rule-based choreography models and RNN-based sequence generators have the drawbacks of generating stiff, repetitive, and less expressive outputs, and GAN-based methods have the setbacks of training instability and motion collapse. To overcome this aforementioned challenge, Residual Attention Temporal Gated Networks (RAST-Gated Networks) is introduced, which integrates residual temporal learning, attention-based feature fusion, and transformer-based sequence modelling to learn long-term dependencies between audio and motion with realism. The model is trained on huge Kaggle Dance datasets with spectral augmentations and pose-based augmentations and is benchmarked with accuracy and F1-score against baseline architectures. Experimental results prove that performance is better with 97.6 % accuracy and 96.8 % F1-score, proving the robustness of the model in rhythm alignment, motion fluidity, and cross-genre adaptability, thus guaranteeing a more expressive and human-like dance movement generation mechanism.