Biomechanical analysis of cinematic motion: AI-driven generation and evaluation in film and animation

Abstract

Human motion synthesis plays a central role in film and animation, where motion quality influences both narrative coherence and perceptual realism. While data-driven deep learning models have shown promise in automating motion generation, they often lack biomechanical fidelity, leading to physically implausible results such as limb distortion and foot sliding. To address these challenges, we propose BCMG-Net, a Biomechanically Constrained Motion Generation Network that embeds anatomical and kinetic priors into a Transformer-based architecture. Our model integrates bone length preservation, dynamic smoothness, and energy efficiency constraints directly into the training objective, ensuring structural consistency and motion naturalness. Moreover, semantic control vectors enable context-aware generation for diverse cinematic actions. Experiments conducted on Human3.6 M, CMU MoCap, and a curated film motion dataset demonstrate that BCMG-Net outperforms state-of-the-art baselines across multiple biomechanical and perceptual metrics. Joint range heatmaps, center of mass trajectories, and motion embedding analyses further validate the physical coherence of the generated motion. These results establish BCMG-Net as a practical and principled framework for physically grounded motion synthesis in high-fidelity digital storytelling.