CLIPVG: Text-Guided Image Manipulation Using Differentiable Vector Graphics
DOI:
https://doi.org/10.1609/aaai.v37i2.25326Keywords:
CV: Applications, CV: Language and Vision, ML: Unsupervised & Self-Supervised LearningAbstract
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.Downloads
Published
2023-06-26
How to Cite
Song, Y., Shao, X., Chen, K., Zhang, W., Jing, Z., & Li, M. (2023). CLIPVG: Text-Guided Image Manipulation Using Differentiable Vector Graphics. Proceedings of the AAAI Conference on Artificial Intelligence, 37(2), 2312-2320. https://doi.org/10.1609/aaai.v37i2.25326
Issue
Section
AAAI Technical Track on Computer Vision II