BABEL is a large dataset with language labels describing the actions being performed in mocap sequences. BABEL consists of action labels for about 43 hours of mocap sequences from AMASS. Action labels are at two levels of abstraction -- sequence labels describe the overall action in the sequence, and frame labels describe all actions in every frame of the sequence. Each frame label is precisely aligned with the duration of the corresponding action in the mocap sequence, and multiple actions can overlap. There are over 28k sequence labels, and 63k frame labels in BABEL, which belong to over 250 unique action categories. Labels from BABEL can be leveraged for tasks like action recognition, temporal action localization, motion synthesis, etc.
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Toyota Smarthome Trimmed has been designed for the activity classification task of 31 activities. The videos were clipped per activity, resulting in a total of 16,115 short RGB+D video samples. activities were performed in a natural manner. As a result, the dataset poses a unique combination of challenges: high intra-class variation, high-class imbalance, and activities with similar motion and high duration variance. Activities were annotated with both coarse and fine-grained labels. These characteristics differentiate Toyota Smarthome Trimmed from other datasets for activity classification.
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InfiniteRep is a synthetic, open-source dataset for fitness and physical therapy (PT) applications. It includes 1k videos of diverse avatars performing multiple repetitions of common exercises. It includes significant variation in the environment, lighting conditions, avatar demographics, and movement trajectories. From cadence to kinematic trajectory, each rep is done slightly differently -- just like real humans. InfiniteRep videos are accompanied by a rich set of pixel-perfect labels and annotations, including frame-specific repetition counts.
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