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Pose2Muscle reformulates muscle estimation as a structured prediction problem over discrete muscle activity states, yielding a more stable and interpretable target space and suggesting the potential of Pose2Muscle for muscle-aware movement analysis when direct physiological sensing is impractical.
The proposed FlowGen uses LLM-based Semantic Information Processing (SIP) to extract semantic elements, constructs a Semantic Relational Graph (SRG) encoded by an enhanced R-GAT for basic flow generation (BFGen), and further supports branch point prediction through BPP and branch-conditioned alternative flow generation through AFGen.
