With MuscleMap136, we discover limitations of state-of-the-art architectures for human activity recognition when dealing with multi-label muscle annotations and good generalization to unseen activities is required. We further complement the main MuscleMap136 dataset, which specifically targets physical exercise, with Muscle-UCF90 and Muscle-HMDB41, which are new variants of the well-known activity recognition benchmarks extended with AMGE annotations. This dataset opens the vistas to multiple video-based applications in sports and rehabilitation medicine. To this intent, we provide the MuscleMap136 featuring >15K video clips with 136 different activities and 20 labeled muscle groups. Video-based AMGE is an important yet overlooked problem. In this paper, we tackle the new task of video-based Activated Muscle Group Estimation (AMGE) aiming at identifying currently activated muscular regions of humans performing a specific activity.
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