StyleSketch
PaidStylized Face Sketch Extraction via Generative Prior with Limited Data
About StyleSketch
StyleSketch is a method for extracting high-resolution stylized face sketches from a single face image, leveraging the deep features of a pretrained StyleGAN. It can be trained with as few as 16 pairs of face and corresponding sketch images, using part-based losses and a two-stage learning strategy for fast convergence. The approach outperforms existing state-of-the-art sketch extraction and few-shot image adaptation methods, and can be extended to other domains (e.g., animals, objects) while also enabling semantic editing of the resulting sketches.
Key Features
Pros & Cons
- Requires only 16 paired images for training, significantly less than competing methods
- Outperforms existing state-of-the-art sketch extraction and few-shot adaptation methods
- Generates high-resolution (1024×1024) abstract sketches
- Enables semantic editing of the extracted sketch via StyleGAN's feature space
- Applicable to multiple domains without retraining the full model
- Relies on a pretrained StyleGAN, which may not be available for all domains
- Sketch extraction requires GAN inversion of the input image, adding computational overhead
- Training requires paired sketch data (face + sketch), which may be difficult to obtain for arbitrary styles
- Primarily designed for face sketches; extension to other domains may require additional tuning
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