Abstract

In the ever-evolving world of deep learning, creating photorealistic photo editing poses is challenging, especially when modifying features such as hairstyles in photos. The system leverages advanced generative adversarial networks (GANs) to solve problems such as misalignment, texturing, and lighting conflicts. A dedicated color adjustment module controls hair color change even under different lighting conditions, while a refinement module restores fine details for highly realistic final images. Recent solutions have shown significant improvements in both speed and accuracy. These advances are paving the way for more implementation in areas like virtual experiments, interactive tournaments, and design tools. In this survey, we examine the most advanced deep learning techniques for processing real-life images, focusing on their ability to handle complex transformations like hair editing.

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