CLIPVG：使用可微分矢量图形的文本引导图像编辑
CLIPVG: Text-Guided Image Manipulation Using Differentiable Vector Graphics
AI总结：
提出CLIPVG，首个无需额外生成模型的基于CLIP的通用图像编辑框架，利用可微分矢量图形实现文本引导编辑，在语义正确性和合成质量上达到最先进水平，并支持多种应用。
摘要：
最近，利用CLIP（对比语言-图像预训练）模型进行文本引导图像编辑方面取得了显著进展。然而，所有现有方法都依赖额外的生成模型来确保结果质量，因为仅凭CLIP无法为精细尺度的像素级变化提供足够的指导信息。在本文中，我们提出了CLIPVG，一个使用可微分矢量图形的文本引导图像编辑框架，这也是首个基于CLIP的、无需任何额外生成模型的通用图像编辑框架。我们证明，CLIPVG不仅在语义正确性和合成质量上达到了最先进的性能，而且足够灵活，能够支持远超所有现有方法能力的各种应用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
