The field of computer vision is witnessing significant developments in color transfer and style representation. Researchers are exploring innovative approaches to automate the process of transforming sketches into vividly-colored images, conditioned on reference images or styles. The direction of the field is shifting towards achieving accurate color matching, multi-instance control, and semantic coherence. Noteworthy papers in this area include MagicColor, which enables automatic transformation of sketches into colored images with accurate consistency and multi-instance control. Other notable works, such as Color Conditional Generation with Sliced Wasserstein Guidance and Semantix, are proposing novel methods for color-conditional generation and semantic style transfer, respectively.