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Abstract Wushu instruction still rests on the coach’s eye and on repeated demonstration, an approach that resists quantification, delivers feedback only after a routine ends, and cannot retune difficulty for each learner. We therefore asked a deliberately narrow question: does a training paradigm in which biomechanically grounded features drive a reinforcement learning policy improve skill acquisition more than conventional coaching?
Abstract Traditional martial arts are hard to hand on. The masters who hold the repertoire are ageing, instruction rarely travels beyond the training hall, and verbal correction remains stubbornly subjective. This paper presents an intelligent virtual coaching system that couples enhanced three-dimensional pose estimation with generative adversarial network refinement.
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