The paper introduces an end‑to‑end automated pipeline that generates controllable crack data for deep‑learning inspection. It uses procedurally sampled Bézier‑curve skeletons converted into realistic crack masks via a GAN, and a dual‑ControlNet diffusion model that separates appearance from geometry while enforcing boundary consistency. The method supports both background‑free synthesis and context‑aware inpainting, and shows improved performance over existing augmentation baselines on CRACK500 and CrackTree200 datasets.
By Conghui Li, Muxin Pu, Chern Hong Lim, Weiyao Lin, Xin Wang
The paper presents a hybrid generative model that combines denoising diffusion and adversarial training to produce 3D geological microstructures from 2D images. It addresses limitations of previous GAN-based methods like SliceGAN, particularly for heterogeneous materials, by replacing the denoising loss with an adversarial loss to achieve stable training. The resulting model generates microstructures with minimal slice artefacts and accurate phase fractions and structural descriptors.
By Ali Aouf, Eric Laloy, Bart Rogiers, Christophe De Vleeschouwer
arXiv:2605. 31162v1 Announce Type: cross Abstract: Unconditional diffusion models offer powerful generative priors, yet steering them toward aesthetically enhanced outputs remains largely unexplored.
By Shreyansh Modi, Akshat Tomar, Aarush Aggarwal
The paper reports a new phenomenon in CLIP embeddings where human and AI‑generated paintings naturally separate along dominant principal directions without any supervised training. The authors investigate this separation by linking embedding directions back to image features using interpretable representations and gradient‑based inversion, finding that the separation is driven by distributed multiscale image structure rather than simple global or local statistics. They also show that small, imperceptible image perturbations can cause large displacements along these directions, highlighting a mismatch between CLIP’s visual evidence and human perception.
By Andrea Asperti
arXiv:2204. 14224v3 Announce Type: replace-cross Abstract: The automated analysis of heterogeneous natural textures is frequently hindered by physical damage and data loss, presenting a significant challenge to computer vision.
By Galymzhan Abdimanap, Kairat Bostanbekov, Abdelrahman Abdallah, Anel Alimova, Darkhan Kurmangaliyev, Daniyar Nurseitov, Tatyana Dedova, Larissa Balakay, Serik Nurakynov
arXiv:2605.16879v2 Announce Type: replace
Abstract: With the rapid evolution of synthetic media, Image Manipulation Localization (IML) has emerged as a critical component in multimedia forensics for...
By Yunfei Wang, Bo Du, Zhe Yang, Xin Liu, Zhiyu Lin, Tianxin Xu, Ji-Zhe Zhou
arXiv:2606. 08033v1 Announce Type: cross Abstract: Cracks are a critical indicator of building health, and early stage identification is fundamental to prevent harmful damages.
By Mattia Forlesi, Alfonso Esposito, Ivan Zyrianoff, Alessandro Marzani, Marco Di Felice
arXiv:2606. 04299v1 Announce Type: cross Abstract: We consider the problem of generating images whose internal structure -- defined by the distribution of patches across multiple scales -- matches that of a single reference image.
By Haojun Qiu, Kiriakos N. Kutulakos, David B. Lindell
arXiv:2204.11531v2 Announce Type: replace
Abstract: Invariance to diverse types of image corruption, such as noise, blurring, or colour shifts, is essential to establish robust models in computer vis...
By Minghui Chen, Cheng Wen, Feng Zheng, Fengxiang He, Ling Shao
arXiv:2605. 23264v2 Announce Type: replace-cross Abstract: Generative priors in Image Super-Resolution (SR) often compromise faithful restoration, we attribute this limitation to a fundamental spectral misalignment between isotropic objectives and the intrinsic natural image manifold.
By Hongbo Wang, Huaibo Huang, Pin Wang, Jinhua Hao, Chao Zhou, Ran He
arXiv:2605.11585v2 Announce Type: replace
Abstract: This paper addresses the problem of image denoising for grayscale images. We propose a probabilistic image generative model that combines a quadtre...
By Shota Saito, Yuta Nakahara, Kohei Horinouchi, Naoki Ichijo, Manabu Kobayashi, Toshiyasu Matsushima
arXiv:2410.07421v2 Announce Type: replace
Abstract: Instance segmentation is a core computer vision task with great practical significance. Recent advances, driven by large-scale benchmark datasets,...
By Przemyslaw Polewski, Jacquelyn Shelton, Wei Yao, Marco Heurich