应用于人脸图像校正的模糊条件扩散与扩散投影注意力
Fuzzy-Conditioned Diffusion and Diffusion Projection Attention Applied to Facial Image Correction
AI总结：
本文提出模糊条件扩散与扩散投影注意力机制，通过逐像素可控强度修改和异常程度估计，实现了可解释且自主的人脸图像校正。
摘要：
图像扩散近期在图像合成中展现出卓越性能，并隐式地作为图像先验。该先验已与条件结合用于解决图像修复问题，但仅支持基于用户的二值条件。我们推导出模糊条件扩散，其中隐式扩散先验能以可控强度被利用。我们的模糊条件可逐像素应用，使得不同图像组件能被不同程度地修改。此外，我们提出一种人脸图像校正应用，将我们的模糊条件扩散与扩散衍生的注意力图相结合。我们的图估计异常程度，并通过在扩散空间上投影来获得它。我们展示了该方法如何实现可解释且自主的人脸图像校正。
英文摘要：
Image diffusion has recently shown remarkable performance in image synthesis and implicitly as an image prior. Such a prior has been used with conditioning to solve the inpainting problem, but only supporting binary user-based conditioning. We derive a fuzzy-conditioned diffusion, where implicit diffusion priors can be exploited with controllable strength. Our fuzzy conditioning can be applied pixel-wise, enabling the modification of different image components to varying degrees. Additionally, we propose an application to facial image correction, where we combine our fuzzy-conditioned diffusion with diffusion-derived attention maps. Our map estimates the degree of anomaly, and we obtain it by projecting on the diffusion space. We show how our approach also leads to interpretable and autonomous facial image correction.
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。
