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An experimental study of animating-based facial image manipulation in online class environments
Jeong-Ha Park1, Chae-Yun Lim2, Hyuk-Yoon Kwon3
1Graduate School of Data Science, Seoul National University of Science and Technology, Seoul, South Korea.
Scientific Reports
|March 23, 2023
Summary
This study introduces an evaluation framework for real-time facial expression swap models in online classes. While effective with large faces, performance degrades with smaller facial regions, impacting image quality and expression accuracy.
Area of Science:
- Computer Vision and Artificial Intelligence
- Human-Computer Interaction
- Digital Media Processing
Background:
- Deepfake technology enables sophisticated facial image manipulation, including expression swapping.
- Expression swapping offers real-time application advantages by altering only facial expressions.
- Online learning environments necessitate robust facial manipulation techniques for engagement and assessment.
Purpose of the Study:
- To propose and evaluate a framework for assessing real-time facial expression swap models in online class settings.
- To define scenarios simulating online class situations (attendance, presentation, examination) based on facial visibility.
- To compare the performance of the First Order Model and GANimation for expression swapping under varying conditions.
Main Methods:
- Development of an evaluation framework accepting a source image and target video for expression manipulation.
- Implementation and testing of two expression swap models: First Order Model and GANimation.
- Quantitative and qualitative performance analysis across three defined scenarios with different facial portions.
Main Results:
- Both models performed acceptably in Scenario 1 (large face visibility).
- Performance significantly degraded in Scenarios 2 and 3 (smaller face visibility).
- First Order Model maintained better image quality, while GANimation showed superior expression change representation.
Conclusions:
- Facial region size critically impacts expression swap model performance in online class contexts.
- A hybrid approach or model optimization is needed for reliable real-time expression swapping with smaller faces.
- Demonstrated feasibility of integrating expression swap technology into platforms like Zoom, Google Meet, and Microsoft Teams for enhanced online interactions.
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