  Considerable progress has recently been made in leveraging CLIP (Contrastive
Language-Image Pre-Training) models for text-guided image manipulation.
However, all existing works rely on additional generative models to ensure the
quality of results, because CLIP alone cannot provide enough guidance
information for fine-scale pixel-level changes. In this paper, we introduce
CLIPVG, a text-guided image manipulation framework using differentiable vector
graphics, which is also the first CLIP-based general image manipulation
framework that does not require any additional generative models. We
demonstrate that CLIPVG can not only achieve state-of-art performance in both
semantic correctness and synthesis quality, but also is flexible enough to
support various applications far beyond the capability of all existing methods.
