Structured Kinematic Routing for Unified Action Difference Reasoning

Analyzing fine-grained execution differences in skilled activities, such as sports, demands moving beyond holistic feature extraction to precise, domain-specific visual reasoning. While prior work has made progress in evaluating individual performance, existing methods fall short in comparing two similar actions (e.g., penalty kick in soccer) conducted by different performers and explaining how their actions differ. To address this gap, we introduce Unified Action Difference Reasoning (UADR), a novel task that unifies quantitative performance scores and qualitative explanations of inter-performer differences, enabling actionable feedback for improvement. To support this task, we construct the ADR dataset, built upon Ego-Exo4D dataset, comprising paired videos annotated with both performance scores and natural language descriptions of action differences. We further propose Structured Kinematic Routing (SKR), a differentiable kinematic routing framework that models the visual reasoning process behind performance differences by capturing fine-grained kinematic cues. SKR discovers an optimal visual trajectory through a hierarchical kinematic ontology, regularizes these paths via cross-modal semantic grounding using expert commentary priors, and captures counterfactual topological deviations through dynamic relational discrepancy modeling. Comprehensive experiments demonstrate that SKR significantly outperforms state-of-the-art vision-language models and baseline approaches on the UADR task. Furthermore, our framework generalizes effectively to traditional Action Quality Assessment (AQA) settings, surpassing state-of-the-art approaches on benchmarks including T3SeT and FitnessAQA. Code, model and dataset will be released upon the completion of review process.