A paper from our group got accepted to ICCV 2025 as an ORAL!
Hamadi Chihaoui, Paolo Favaro, in the Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025.
Zero-shot image restoration (IR) methods based on pretrained diffusion models have recently achieved significant success. These methods typically require at least a parametric form of the degradation model. However, in real-world scenarios, the degradation may be too complex to define explicitly without relying on crude approximations. To handle this general case, we introduce the DIffusion Image Prior (DIIP). We take inspiration from the Deep Image Prior (DIP), since it can be used to remove artifacts without the need for an explicit degradation model. However, in contrast to DIP, we find that pretrained diffusion models offer a much stronger prior, despite being trained without knowledge from corrupted data. We show that, the optimization process in DIIP first reconstructs a clean version of the image before eventually overfitting to the degraded input, but it does so for a broader range of degradations than DIP. In light of this result, we propose a blind image restoration (IR) method based on early stopping, which does not require prior knowledge of the degradation model. We validate DIIP on various degradation-blind IR tasks, including JPEG artifact removal, waterdrop removal, denoising and super-resolution with state-of-the-art results.
Llukman Cerkezi, Aram Davtyan, Sepehr Sameni, Paolo Favaro, in the Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025.
This paper introduces a novel pipeline to generate novel views of an object from a single image. Our method, MIRAGE, trains a pose-conditioned diffusion model on a dataset of real images of multiple unknown categories, all completely unsupervised. The conditioning is obtained via clustering pre-trained self-supervised features to identify approximate object categories and poses. At inference time, we introduce hard-attention guidance and apply cross-view attention to align the appearance of the objects in the generated views with that in the input image. Through our experiments, we show that MIRAGE generates novel views that are on par or better than supervised methods in terms of image realism and 3D consistency. Furthermore, MIRAGE is robust to diverse textures and geometries, not restricted to simple rigid rotations, and is capable of generating plausible deformations of nonrigid objects, such as animals.
Code: https://github.com/llukmancerkezi/mirage