Dance Movement Style Transfer Based on 3D Pose Estimation and Spatiotemporal Feature Fusion

This study presents a dance movement style transfer method based on 3D pose estimation and spatiotemporal feature fusion. The proposed method aims to preserve the structural characteristics and temporal coherence of source dance movements while adapting them to a target style. To address the challenges of movement fidelity, style consistency, and motion smoothness, the framework employs a Manifold Event Forecaster as the core module. This module integrates a Counterfactual Manifold Optimizer, Modeling Discrete Dance Events, and a Comprehensive Framework for Dance Motion Transformation to predict and transform dance motion sequences in a style-aware manner. The Counterfactual Manifold Optimizer guides the generation of target-style movement trajectories in the motion manifold, while the discrete event modeling module segments key dance events for more precise motion transformation. Stochastic refinement is introduced to improve the robustness of style transfer under motion variability and pose uncertainty. Experimental evaluations show that the proposed method can generate coherent target-style dance movements while maintaining the choreographic structure and motion characteristics of the original sequence. The study provides a feasible framework for dance movement style transfer and offers potential applications in digital choreography, motion synthesis, and creative performance analysis.