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Real-time data-efficient portrait stylization via geometric alignment.

Xinrui Wang1, Zhuoru Li2, Xuanyu Yin3

  • 1The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 110-0033, Japan.

Neural Networks : the Official Journal of the International Neural Network Society
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Summary
This summary is machine-generated.

This study introduces a new portrait stylization method using differentiable Thin-Plate-Spline (TPS) modules within a Generative Adversarial Network (GAN). This approach enhances geometric consistency and achieves real-time stylization on mobile devices.

Keywords:
Generative adversarial networksImage-to-image translationPortrait stylizationStyle transfer

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Existing portrait stylization methods struggle with geometric consistency and feature distribution disparities.
  • These limitations hinder applications on rare styles and mobile devices due to computational constraints.

Purpose of the Study:

  • To develop a novel portrait stylization technique that ensures geometric consistency and improves stylization quality.
  • To create an efficient and lightweight model suitable for real-time inference on mobile devices.

Main Methods:

  • Integration of differentiable Thin-Plate-Spline (TPS) modules into an end-to-end Generative Adversarial Network (GAN) framework.
  • Leveraging facial landmarks for geometric alignment at global and local scales in pixel and feature spaces.

Main Results:

  • Achieved superior fidelity and stylistic consistency compared to existing methods.
  • Demonstrated 2x improvement in training data efficiency and 100x reduction in computational complexity.
  • Enabled real-time inference (30 FPS) at 512x512 resolution on mobile devices.

Conclusions:

  • The proposed TPS-integrated GAN framework effectively addresses geometric inconsistencies in portrait stylization.
  • The lightweight model offers a practical solution for high-quality, real-time portrait stylization on resource-constrained devices.