: Using skeletal data instead of raw video protects privacy and significantly reduces the computational cost of training "data-hungry" deep learning models. Comparison of Skeletal Feature Applications

: A secondary model (Attr-LSTM) then populates this skeleton with specific deep features like colors, textures, and styles to create a rich, final caption. 2. Human Action Recognition (Skeleton-Guided Features)

Instead of processing raw video pixels, models extract (coordinates of joints like elbows and knees) to identify human behavior:

: CNNs and LSTMs extract spatiotemporal features from these moving coordinates to recognize patterns like gait or specific gestures.