Deep image colorization methods for press-on nails: overview and evaluation

Abstract

Colorization techniques have traditionally been trained on datasets like ImageNet and COCO, which focus on natural scenes with large-scale, diverse elements. However, the ability of these models to generalize to domains vastly different from these datasets re- mains underexplored. This research addresses this gap by investigating how colorization methods perform when applied to press-on nails, a rapidly growing and highly creative domain characterized by intricate textures, small-scale designs, and detailed embel- lishments such as charms and 3D elements. We evaluate several colorization models, analyzing their architectural frameworks, generalization capabilities, and performance in meeting the unique challenges of press-on nail designs. Our goal is to assess whether these AI-driven techniques can support nail artists by streamlining the design process and enabling more efficient, personalized, and innovative creative workflows. Through this study, we aim to provide insights into the potential of AI-assisted colorization to enhance the press-on nail market.