Transformer-based video generation technique for biomechanical motion analysis
Abstract
In this study, a Transformer-based video generation technique is proposed for accurately modelling biomechanical movement patterns, and its performance is systematically evaluated in walking, running, throwing and other movement tasks. The experimental results show that Transformer outperforms traditional methods (RNN, CNN, GAN) in terms of motion trajectory consistency, temporal synchronization, and video clarity, and is capable of generating high-quality motion videos that comply with biomechanical constraints. This study not only expands the application scope of Transformer in biomechanical analyses, but also provides high-precision solutions for tasks such as gait reconstruction, abnormality detection, rehabilitation training, and motion prediction.
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