Vector Logo Image Synthesis Using Differentiable Renderer

Ryuta Yamakura, Keiji Yanai; Proceedings of the Asian Conference on Computer Vision (ACCV) Workshops, 2024, pp. 543-556

Vector images are widely used in the design field, particularly for logos and icons, due to their scalable properties. Consequently, the flexible and high-quality creation of such images is expected to support creative activities. In this study, we leverage a recently proposed differentiable renderer and the strong raster image generation capabilities of Stable Diffusion to generate vector-format logo images. This is achieved through optimizing vector parameters based on losses calculated from text prompts and shape images. Additionally, we address the self-intersection issue, a common challenge in vector image generation through optimization methods, by introducing a new technique called Radiation Loss. This approach explicitly monitors control points to enhance the quality of the output. While this method successfully generates logo images that maintain the input text and shape, challenges remain, including the persistence of unnecessary paths and difficulty in controlling the output entirely by text prompts. The experimental results showed the effectiveness of the proposed methods.