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Impression Management Techniques IV: Altercasting01:14

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Altercasting is a strategic communication technique in which an individual imposes a specific identity or social role onto another person to influence their behavior and shape the interaction. By presuming a role—such as “responsible leader” or “patient person”—altercasting encourages the target to conform to that identity, often aligning their behavior with the expectations associated with the role. The power of this tactic lies in its subtlety; once a role...
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Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
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GuidedStyle: Attribute knowledge guided style manipulation for semantic face editing.

Xianxu Hou1, Xiaokang Zhang1, Hanbang Liang1

  • 1Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China; Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen, China.

Neural Networks : the Official Journal of the International Neural Network Society
|November 12, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces GuidedStyle, a new framework for controllable semantic face editing using Generative Adversarial Networks (GANs). It enables precise manipulation of facial attributes in generated images.

Keywords:
Generative Adversarial NetworksSemantic face editingStyleGAN

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Unconditional Generative Adversarial Networks (GANs) generate realistic faces but lack semantic control.
  • Existing methods struggle with precise and disentangled control over facial attributes during image synthesis.

Purpose of the Study:

  • To develop a novel framework, GuidedStyle, for semantic face editing on pretrained StyleGAN models.
  • To enable controllable and disentangled manipulation of various facial attributes.

Main Methods:

  • Guiding the StyleGAN generator with a knowledge network.
  • Incorporating an attention mechanism for adaptive layer selection in StyleGAN.
  • Utilizing a pretrained StyleGAN architecture for image generation.

Main Results:

  • Achieved disentangled and controllable edits for attributes like smiling, eyeglasses, gender, mustache, hair color, and attractiveness.
  • Demonstrated superiority over competing methods in both qualitative and quantitative evaluations.
  • Showcased strong generalization ability on real and artistic face editing tasks.

Conclusions:

  • GuidedStyle offers effective semantic face editing by controlling the StyleGAN generation process.
  • The proposed attention mechanism enhances disentanglement and controllability.
  • The framework shows broad applicability across diverse face editing scenarios.