CartoonTalk: A Speech-Driven Animation Model for Cartoon Face Generation.

Area of Science:

• Computer Vision
• Computer Graphics
• Artificial Intelligence

Background:

• Existing speech-driven animation models struggle with cartoon faces due to appearance and movement differences.
• Direct application leads to inaccurate key point capture, distorted expressions, and animation artifacts.

Purpose of the Study:

• To propose CartoonTalk, a speech-driven cartoon face animation generation model.
• To address limitations in current techniques for animating cartoon characters from speech.

Main Methods:

• The CartoonTalk model integrates 2D cartoon key points with 3D motion coefficients.
• It comprises three modules: face annotation (2D key points), motion extraction (3D coefficients from speech), and 3D facial rendering.

Main Results:

• CartoonTalk significantly outperforms existing approaches in lip-sync accuracy.
• The model achieves superior visual quality in generated cartoon face animations.
• New state-of-the-art benchmarks were established on cartoon datasets.

Conclusions:

• CartoonTalk offers an effective solution for speech-driven cartoon face animation.
• The integration of 2D and 3D features enhances animation fidelity.
• The model sets a new standard for realistic cartoon character lip-sync and expression.