StyleSketch: Stylized Face Sketch Extraction via Generative Prior with Limited Data

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StyleSketch is a method for extracting high-resolution stylized sketches from a face image. Using the rich semantics of the deep features from a pretrained StyleGAN, StyleSketch can train a sketch generator with just 16 pairs of face and corresponding sketch images.

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StyleSketch: Stylized Face Sketch Extraction via Generative Prior with Limited Data

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Updated:2024/10/02

What is StyleSketch: Stylized Face Sketch Extraction via Generative Prior with Limited Data

StyleSketch is a novel method for extracting high-resolution stylized sketches from face images using a pretrained StyleGAN and limited data. It outperforms existing state-of-the-art methods and can be extended to other domains.

Key Features of StyleSketch: Stylized Face Sketch Extraction via Generative Prior with Limited Data

High-resolution stylized sketch extraction, part-based losses with two-stage learning, fast convergence during training, and versatility in other domains.

High-Resolution Sketch Extraction

StyleSketch can extract high-resolution stylized sketches from face images using a pretrained StyleGAN and limited data.

Part-Based Losses with Two-Stage Learning

StyleSketch trains a sketch generator using part-based losses with two-stage learning for fast convergence during training for high-quality sketch extraction.

Versatility in Other Domains

StyleSketch can be extended to other domains for sketch extraction and semantic editing.

Fast Convergence During Training

StyleSketch's two-stage learning approach enables fast convergence during training for high-quality sketch extraction.

Semantic Editing

StyleSketch allows for semantic editing of face sketches, enabling users to modify specific attributes of the sketch.

Use Cases of StyleSketch: Stylized Face Sketch Extraction via Generative Prior with Limited Data

Extracting high-resolution stylized sketches from face images for artistic purposes.
Using StyleSketch for sketch extraction and semantic editing in other domains, such as product design or architecture.
Applying StyleSketch to generate abstract face sketches for use in various applications, such as video games or animations.
Utilizing StyleSketch to create personalized avatars or characters for virtual reality or social media platforms.

Pros and Cons of StyleSketch: Stylized Face Sketch Extraction via Generative Prior with Limited Data

Pros

High-quality sketch extraction with limited data.
Fast convergence during training for efficient processing.
Versatility in other domains for sketch extraction and semantic editing.
Semantic editing capabilities for modifying specific attributes of the sketch.

Cons

Requires a pretrained StyleGAN for optimal performance.
May require additional fine-tuning for specific use cases or domains.
Limited control over the style of the generated sketch.
May not work well with low-quality or noisy input images.

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How to Use StyleSketch: Stylized Face Sketch Extraction via Generative Prior with Limited Data

1
Train a sketch generator using StyleSketch's part-based losses with two-stage learning.
2
Feed the input image into the trained sketch generator to extract a high-resolution stylized sketch.
3
Use StyleSketch's semantic editing capabilities to modify specific attributes of the sketch.
4
Extend StyleSketch to other domains for sketch extraction and semantic editing.

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StyleSketch: Stylized Face Sketch Extraction via Generative Prior with Limited Data

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StyleSketch: Stylized Face Sketch Extraction via Generative Prior with Limited Data

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What is StyleSketch: Stylized Face Sketch Extraction via Generative Prior with Limited Data