Analysis of head gesture and prosody patterns for prosody-driven head-gesture animation

Area of Science:

• Computer Vision
• Speech Processing
• Human-Computer Interaction

Background:

• Realistic head gesture synthesis from speech is crucial for improving virtual agents and telepresence.
• Existing methods often lack naturalness or require extensive manual annotation.

Purpose of the Study:

• To propose a novel two-stage framework for the joint analysis and synthesis of head gestures from speech prosody.
• To achieve automatic and realistic synthesis of head gestures synchronized with speech.

Main Methods:

• Utilized Hidden Markov Models (HMMs) for unsupervised temporal segmentation of head gesture and speech prosody features.
• Employed Multi-Stream HMMs for joint analysis of correlations between elementary head gesture and prosody patterns.
• Developed an audio-visual mapping model for synthesizing head gestures from speech prosody.

Main Results:

• The proposed framework successfully synthesizes natural-looking head gestures from arbitrary input speech.
• Evaluations confirmed the effectiveness in both standard synthesis and "prosody transplant" and "gesture transplant" scenarios.
• The synthesized gestures were found to be natural and synchronized with the input speech.

Conclusions:

• The synthesis-by-analysis framework provides a robust method for realistic head gesture generation.
• This approach enhances the expressiveness of virtual characters and communication systems.
• The model demonstrates versatility across different speech and gesture scenarios.