SketchFFusion：基于扩散模型的草图引导图像编辑
SketchFFusion: Sketch-guided image editing with diffusion model
AI总结：
针对草图引导图像编辑中草图获取难及现有方法失真、细节丢失等问题，提出保留主轮廓且贴合用户风格的草图生成方案及基于结构向量的条件扩散模型SketchFFusion，性能优于现有方法。
摘要：
草图引导的图像编辑旨在根据用户提供的草图信息对图像进行局部微调，同时保持未编辑区域的原始状态。由于获取人工草图的成本较高，以往的工作大多依赖边缘图作为草图的替代品，但草图包含更丰富的结构信息。本文提出了一种草图生成方案，该方案能够保留图像的主要轮廓，并紧密贴合用户绘制的实际草图风格。同时，当前的图像编辑方法常常面临图像失真、训练成本高以及草图中精细细节丢失等挑战。为解决这些局限，我们提出了一种基于草图结构向量的条件扩散模型（SketchFFusion）。我们评估了该模型的生成性能，并证明其优于现有方法。
英文摘要：
Sketch-guided image editing aims to achieve local fine-tuning of the image based on the sketch information provided by the user, while maintaining the original status of the unedited areas. Due to the high cost of acquiring human sketches, previous works mostly relied on edge maps as a substitute for sketches, but sketches possess more rich structural information. In this paper, we propose a sketch generation scheme that can preserve the main contours of an image and closely adhere to the actual sketch style drawn by the user. Simultaneously, current image editing methods often face challenges such as image distortion, training cost, and loss of fine details in the sketch. To address these limitations, We propose a conditional diffusion model (SketchFFusion) based on the sketch structure vector. We evaluate the generative performance of our model and demonstrate that it outperforms 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。
