MuscleMap: Towards Video-based Activated Muscle Group Estimation

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. Video-based AMGE is an important yet overlooked problem. To this intent, we provide the MuscleMap136 featuring >15K video clips with 136 different activities and 20 labeled muscle groups. This dataset opens the vistas to multiple video-based applications in sports and rehabilitation medicine. 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. 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. To address this, we propose a new multimodal transformer-based model, TransM3E, which surpasses current activity recognition models for AMGE, especially as it comes to dealing with previously unseen activities. The datasets and code will be publicly available at https://github.com/KPeng9510/MuscleMap.