Gesture Sequence

Gesture sequence research focuses on understanding and generating sequences of human gestures, aiming to improve human-computer interaction and robot control, as well as analyze gestures for applications like autism detection. Current research employs diverse model architectures, including transformers, diffusion models, and recurrent neural networks, often combined with techniques like quantization and attention mechanisms to improve efficiency and realism in gesture synthesis and recognition. This field is significant for advancing human-robot interaction, enabling more intuitive control of robots and virtual avatars, and providing novel tools for analyzing human behavior and communication.