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A Semantic Segment Encoder (SSE): Improving human face inversion quality through minimized learning space.

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This study introduces a new semantic segment encoder (SSE) to improve StyleGAN face inversion. The SSE enhances image quality by editing specific facial segments, overcoming limitations of current methods.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Generative Adversarial Networks (GANs) are advanced tools for image synthesis.
  • Style-Based Generator Architecture for Generative Adversarial Networks (StyleGAN) is prominent in human face manipulation.
  • Existing StyleGAN methods face challenges with the distortion-edit tradeoff in latent space, limiting real-world applications.

Purpose of the Study:

  • To enhance the quality of human face inversion using StyleGAN.
  • To address the distortion-edit tradeoff problem in StyleGAN's latent space.
  • To introduce a novel semantic segment encoder (SSE) for improved face restoration.

Main Methods:

  • Proposing a novel semantic segment encoder (SSE) that narrows the restoration latent space.
  • Minimizing the encoder's learning area to human-recognizable semantic-segment units.
  • Editing only one segment at a time to prevent interference with other facial features.

Main Results:

  • The proposed SSE significantly improved face inversion quality.
  • Distortion quality was enhanced by approximately 20% compared to existing methods.
  • Editing performance was maintained without compromising image quality.

Conclusions:

  • The novel semantic segment encoder (SSE) effectively improves face inversion quality in StyleGAN.
  • By focusing on semantic segments, the SSE overcomes the distortion-edit tradeoff.
  • This approach offers a promising solution for practical applications requiring high-fidelity face manipulation.