Fuzzy-Conditioned Diffusion and Diffusion Projection Attention Applied to Facial Image Correction
This work addresses the need for more flexible and interpretable image correction tools in computer vision, particularly for facial images, but it is incremental as it builds on existing diffusion prior methods.
The authors tackled the problem of limited conditioning in diffusion models for image inpainting by developing fuzzy-conditioned diffusion, which allows controllable strength and pixel-wise modifications, and applied it to facial image correction with diffusion projection attention for anomaly detection, achieving interpretable and autonomous correction.
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.