Zero-Shot Font Style Transfer with a Differentiable Renderer
Kota Izumi, Keiji Yanai · 2022
Recently, a large-scale language-image multi-modal model, CLIP, has been used to realize language-based image translation in a zero-shot manner without training. In this study, we attempted to generate language-based decorative fonts for font images using CLIP. By the existing image style transfer methods using CLIP, stylized font images are usually only surrounded by decorations, and the characters themselves do not change significantly. On the other hand, in this study, we use CLIP and vector graphics image representation using a differentiable renderer to achieve a style transfer of text images that matches the input text. The experimental results show that the proposed method transfers the style of font images to match the given texts. In addition to text images, we confirmed that the proposed method was also able to transform the style of simple logo patterns based on the given texts.