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IA-FaceS: A bidirectional method for semantic face editing.

Wenjing Huang1, Shikui Tu1, Lei Xu1

  • 1Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China.

Neural Networks : the Official Journal of the International Neural Network Society
|December 8, 2022
PubMed
Summary
This summary is machine-generated.

IA-FaceS offers flexible face editing by disentangling attributes and components. This method achieves high-fidelity reconstruction and detailed manipulation, outperforming existing techniques.

Keywords:
Deep bidirectional methodDisentangled attribute manipulationFacial component editing

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Existing semantic face editing methods struggle with flexible manipulation and high-fidelity reconstruction due to single-code encoding.
  • This limitation leads to entangled face manipulations and restricted editing of specific facial components.

Purpose of the Study:

  • To introduce IA-FaceS, a novel bidirectional method for disentangled face attribute manipulation and controllable component editing.
  • To overcome the limitations of single-code schemes in achieving both flexibility and fidelity in face editing.

Main Methods:

  • IA-FaceS employs a two-branch embedding strategy: one for component-invariant content details and another for component-specific manipulations.
  • A component adaptive modulation (CAM) module is introduced to disentangle correlated face components and guide the decoder.
  • The method enables novel single-eye editing without requiring masks or sketches.

Main Results:

  • IA-FaceS demonstrates a superior balance between preserving image details and enabling flexible face manipulation.
  • Experimental results show improved performance in reconstruction, attribute manipulation, and component transfer compared to existing methods.
  • The proposed method achieves high-quality, component-level editing with faithful reconstruction.

Conclusions:

  • IA-FaceS significantly advances semantic face editing by offering disentangled control over attributes and components.
  • The two-branch approach and CAM module effectively address the challenges of flexibility and fidelity in face editing.
  • The method sets a new standard for controllable and high-quality facial image manipulation.