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Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
Published on: December 24, 2015
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GSmoothFace: Generalized Smooth Talking Face Generation via Fine Grained 3D Face Guidance
Summary
GSmoothFace, a novel talking face generation model, synthesizes smooth lip movements and preserves identity without avatar-specific training. This generalized approach improves realism and lip-synchronization for diverse speakers.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Speech Processing
Background:
- Existing speech-driven talking face generation methods require avatar-specific training and struggle with unstable lip movements, limiting real-world applications.
- Current models often fail to generalize across different speakers or maintain identity preservation during synthesis.
Purpose of the Study:
- To propose GSmoothFace, a two-stage generalized talking face generation model that addresses limitations of existing methods.
- To achieve accurate and smooth lip-synchronization while preserving speaker identity.
- To enable generalization to different speakers without the need for retraining.
Main Methods:
- Developed a two-stage model: Audio to Expression Prediction (A2EP) and Target Adaptive Face Translation (TAFT).
- A2EP module utilizes a transformer to predict expression parameters from audio, learning from fine-grained 3D facial vertices for precise lip-sync.
- TAFT module, enhanced by Morphology Augmented Face Blending (MAFB), modifies target video faces using predicted parameters, preserving identity and background.
Main Results:
- Achieved superior performance in realism, lip-synchronization, and visual quality compared to existing methods.
- Demonstrated effective generalization to different speakers without requiring retraining.
- Successfully synthesized smooth lip dynamics while preserving speaker identity.
Conclusions:
- GSmoothFace offers a significant advancement in generalized talking face generation.
- The model's ability to generalize and maintain identity makes it suitable for real-world applications.
- The proposed method overcomes key challenges in avatar-specific training and lip movement stability.
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