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EAGLE-Eye: Extreme-Pose Action Grader Using Detail Bird's-Eye View
Measuring the quality of a sports action entails attending to the execution of the short-term components as well as the overall impression of the whole program. In this assessment, both appearance clues and pose dynamics features should be involved. Current approaches often treat a sports routine as a simple fine-grained action, while taking little heed of its complex temporal structure. Besides, most of them rely solely on either appearance or pose features to score the performance. In this paper, we present JCA and ADA blocks that are responsible for reasoning about the coordination among the joints and appearance dynamics throughout the performance. We build our two-stream network upon the separate stack of these blocks. The early blocks capture the fine-grained temporal dependencies while the last ones reason about the long-term coarse-grained relations. We further introduce an annotated dataset of sports images with unusual pose configurations to boost the performance of pose estimation in such scenarios. Our experiments show that the proposed method not only outperforms the previous works in short- term action assessment but also is the first to generalize well to minute-long figure-skating scoring.