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