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Related Experiment Video

Updated: Jul 7, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Real-time speech-driven face animation with expressions using neural networks.

Pengyu Hong1, Zhen Wen, T S Huang

  • 1Beckman Inst. for Adv. Sci. and Technol., Illinois Univ., Urbana, IL, USA.

IEEE Transactions on Neural Networks
|February 5, 2008
PubMed
Summary

This study introduces a novel framework for real-time speech-driven synthetic talking faces with expressions. The developed system effectively animates facial expressions, enhancing multimodal communication in virtual environments.

Related Experiment Videos

Last Updated: Jul 7, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Artificial Intelligence

Background:

  • Real-time speech-driven synthetic talking faces are crucial for effective multimodal communication in distributed environments.
  • Nonverbal cues like facial expressions significantly impact human communication and require integration into speech-driven animation systems.

Purpose of the Study:

  • To present a framework for facial deformation modeling, motion analysis, and real-time speech-driven face animation with expressions using neural networks.
  • To develop a quantitative visual representation of facial deformations called motion units (MUs) and an MU-based facial motion tracking algorithm.
  • To construct a real-time audio-to-MUP mapping for expressive face animation.

Main Methods:

  • A framework for facial deformation modeling, automatic facial motion analysis, and real-time speech-driven face animation with expression using neural networks.
  • Learning motion units (MUs) as a quantitative visual representation of facial deformations, approximated by linear combinations of MUs weighted by MU parameters (MUPs).
  • Developing an MU-based facial motion tracking algorithm for audio-visual database collection and training neural networks for real-time audio-to-MUP mapping.

Main Results:

  • A quantitative visual representation of facial deformations using motion units (MUs) and MU parameters (MUPs).
  • An effective real-time audio-to-MUP mapping achieved through neural network training on an audio-visual database.
  • The iFACE system demonstrated real-time speech-driven face animation with expressions, showing comparable effectiveness to real faces in bimodal human emotion perception.

Conclusions:

  • The proposed framework and MU-based approach are effective for real-time speech-driven face animation with expressions.
  • The developed system enhances multimodal communication by generating expressive synthetic talking faces.
  • The synthetic expressive talking faces are comparable to real faces in influencing bimodal human emotion perception.