Speech Driven Gesture

Speech-driven gesture generation aims to create realistic and contextually appropriate nonverbal communication for virtual agents and avatars, enhancing human-computer interaction. Current research heavily utilizes deep learning models, particularly transformer and diffusion architectures, often incorporating techniques like quantization, fuzzy feature extraction, and variational methods to improve gesture naturalness, synchronization with speech, and controllability. These advancements are driven by the need for more expressive and engaging virtual characters in applications ranging from virtual assistants to video games, and are leading to significant improvements in the realism and diversity of synthesized gestures. The field is also actively addressing data limitations through synthetic data generation and exploring multi-modal approaches that integrate text and audio inputs for more nuanced control.