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Face Sketch to Image Generation Using Pix2Pix GAN
Arthik Shetty (R.V. College of Engineering), Sachidanand C G (R.V. College of Engineering), M S Srividya (R.V. College of Engineering)
Abstract: This work presents an automated image synthesis method from hand-drawn sketches using adversarial networks. Conventional translation approaches enforce rigid constraints forcing outputs to match input sketch edges exactly, producing unrealistic results when sketches are imperfect. Our technique utilizes sketches as soft guidance instead of hard constraints, generating more natural images. We introduce a joint completion framework treating sketches as contextual cues for image synthesis. A contextual GAN learns unified sketch-image distributions through shared representation. Key advantages include: (1) joint representation simplifies learning in common sketch-image space, avoiding cross-domain complexities; (2) outputs maintain semantic relation to inputs while exhibiting visual flexibility unlike strict conditional GANs; (3) identical architecture enables bidirectional sketch-image conversion. Experiments on benchmark datasets show improved performance over existing conditional methods, especially for poor-quality sketches, with strong generalization. The method achieves realistic results through context-guided completion rather than direct mapping.