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Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
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ShapeEditor: A StyleGAN Encoder for Stable and High Fidelity Face Swapping.

Shuai Yang1, Kai Qiao1, Ruoxi Qin1

  • 1Henan Key Laboratory of Imaging and Intelligent Processing, People's Liberation Army (PLA) Strategy Support Force Information Engineering University, Zhengzhou, China.

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|February 7, 2022
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Summary

This study introduces ShapeEditor, a novel deep-learning method for face swapping using StyleGAN. It achieves high-quality, stable, and accurate face exchanges by effectively controlling the generator with integrated identity and attribute vectors.

Keywords:
deepfakedisentanglementface swappinggenerative adversarial networkstyle transfer

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Deep Learning

Background:

  • Generative Adversarial Networks (GANs) have advanced many-to-many face swapping.
  • Previous GAN-based methods often suffer from image instability and convergence issues.
  • Existing methods struggle to fully capture the face space distribution.

Purpose of the Study:

  • To propose a novel face-swapping method using a pretrained StyleGAN generator.
  • To address the instability and accuracy issues in current GAN-based face-swapping techniques.
  • To enhance the quality and authenticity of generated swapped images.

Main Methods:

  • Developed a novel face-swapping method leveraging a pretrained StyleGAN generator.
  • Introduced ShapeEditor, a two-step encoder for controlling StyleGAN image generation.
  • Designed an encoding-decoding strategy to integrate source identity and target attributes.

Main Results:

  • The proposed method generates high-quality, clear, and authentic swapped face images.
  • ShapeEditor effectively controls StyleGAN for accurate face exchange.
  • Experimental results demonstrate superiority over state-of-the-art methods in clarity and identity/attribute integration.

Conclusions:

  • The novel face-swapping method based on StyleGAN and ShapeEditor offers improved performance.
  • The approach successfully integrates identity and attribute information for realistic face synthesis.
  • This method represents a significant advancement in stable and accurate deep-learning-based face swapping.