This paper investigates a novel task of generating texture images from perceptual descriptions. Previous work on texture generation focused on either synthesis from examples or generation from procedural models. Generating textures from perceptual attributes have not been well studied yet. Meanwhile, perceptual attributes, such as directionality, regularity and roughness are important factors for human observers to describe a texture. In this paper, we propose a joint deep network model that combines adversarial training and perceptual feature regression for texture generation, while only random noise and user-defined perceptual attributes are required as input. In this model, a preliminary trained convolutional neural network is essentially integrated with the adversarial framework, which can drive the generated textures to possess given perceptual attributes. An important aspect of the proposed model is that, if we change one of the input perceptual features, the corresponding appearance of the generated textures will also be changed. We design several experiments to validate the effectiveness of the proposed method. The results show that the proposed method can produce high quality texture images with desired perceptual properties.
Albert Einstein College of Medicine（阿尔伯特·爱因斯坦医学院）；École Normale Supérieure（巴黎高等师范学院）；PSL University（巴黎文理研究大学）
Texture synthesis models are important tools for understanding visual processing. In particular, statistical approaches based on neurally relevant features have been instrumental in understanding aspects of visual perception and of neural coding. New deep learning-based approaches further improve the quality of synthetic textures. Yet, it is still unclear why deep texture synthesis performs so well, and applications of this new framework to probe visual perception are scarce. Here, we show that distributions of deep convolutional neural network (CNN) activations of a texture are well described…
Indian Institute of Technology Madras（印度理工学院马德拉斯分校）；The University of Queensland（昆士兰大学）
Texture-based studies and designs have been in focus recently. Whisker-based multidimensional surface texture data is missing in the literature. This data is critical for robotics and machine perception algorithms in the classification and regression of textural surfaces. In this study, we present a novel sensor design to acquire multidimensional texture information. The surface texture's roughness and hardness were measured experimentally using sweeping and dabbing. Three machine learning models (SVM, RF, and MLP) showed excellent classification accuracy for the roughness and hardness of surf…
Huazhong University of Science and Technology（华中科技大学）；Tsinghua University（清华大学）
Visual anomaly detection plays a significant role in the development of industrial automatic product quality inspection. As a result of the utmost imbalance in the amount of normal and abnormal data, growing attention has been given to unsupervised methods for defect detection. Although existing reconstruction-based methods have been widely studied recently, establishing a robust reconstruction model for various textured surface defect detection remains a challenging task due to homogeneous and nonregular surface textures. In this paper, we propose a novel unsupervised reconstruction-based met…
Biologically Inspired Dynamic Textures for Probing Motion Perception
CNRS（法国国家科学研究中心）；UNIC（神经信息与复杂性实验室）；Ceremade（Ceremade研究中心）；Univ. Paris-Dauphine（巴黎第九大学）；Institut de Neurosciences de la Timone（蒂莫内神经科学研究所）；UMR 7289 CNRS/Aix-Marseille Université（法国国家科学研究中心/艾克斯-马赛大学 UMR 7289 联合研究单位）
Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception
Department of Neuroscience International School for Advanced Studies (SISSA) Trieste Italy（神经科学系国际先进研究学院（SISSA）特里埃斯特意大利）；Department of Language and Translation Sciences Pompeu Fabra University Barcelona Spain（语言与翻译科学系庞培法布拉大学巴塞罗那西班牙）；ICREA Barcelona Spain（ICREA巴塞罗那西班牙）；Department of Data Science International School for Advanced Studies (SISSA) Trieste Italy（数据科学系国际先进研究学院（SISSA）特里埃斯特意大利）
CTP-Net: Character Texture Perception Network for Document Image Forgery Localization
The College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机与电子工程学院）；The Department of Computer Science, The University of Hong Kong（香港大学计算机科学系）
Perceptual misalignment of texture representations in convolutional neural networks
Neuroscience area International School for Advanced Studies (SISSA) Trieste Italy（国际先进研究学院（SISSA）神经科学部，特里埃斯特，意大利）；Department of Mathematics Informatics and Geosciences Università degli Studi di Trieste Trieste Italy（特里埃斯特大学数学、信息学和地球科学系，特里埃斯特，意大利）；Department of Data Engineering Area Science Park Trieste Italy（数据工程系，Area Science Park，特里埃斯特，意大利）
Adversarial examples reveal the vulnerability and unexplained nature of neural networks. Studying the defense of adversarial examples is of considerable practical importance. Most adversarial examples that misclassify networks are often undetectable by humans. In this paper, we propose a defense model to train the classifier into a human-perception classification model with shape preference. The proposed model comprising a texture transfer network (TTN) and an auxiliary defense generative adversarial networks (GAN) is called Human-perception Auxiliary Defense GAN (HAD-GAN). The TTN is used to …
Indian Institute of Technology Madras（印度理工学院马德拉斯分校）
Human texture perception is a weighted average of multi-sensory inputs: visual and tactile. While the visual sensing mechanism extracts global features, the tactile mechanism complements it by extracting local features. The lack of coupled visuotactile datasets in the literature is a challenge for studying multimodal fusion strategies analogous to human texture perception. This paper presents a visual dataset that augments an existing tactile dataset. We propose a novel deep fusion architecture that fuses visual and tactile data using four types of fusion strategies: summation, concatenation, …
EvTexture++: Event-Driven Texture Enhancement for Video Super-Resolution
MOE Key Laboratory of Brain-Inspired Intelligent Perception and Cognition, University of Science and Technology of China（中国科学技术大学，脑启发智能感知与认知教育部重点实验室）；Midea Group（美的集团）
EvTexture: Event-driven Texture Enhancement for Video Super-Resolution
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
Technical University of Munich（慕尼黑工业大学）；Snap Research（Snap研究院）
We present Text2Tex, a novel method for generating high-quality textures for 3D meshes from the given text prompts. Our method incorporates inpainting into a pre-trained depth-aware image diffusion model to progressively synthesize high resolution partial textures from multiple viewpoints. To avoid accumulating inconsistent and stretched artifacts across views, we dynamically segment the rendered view into a generation mask, which represents the generation status of each visible texel. This partitioned view representation guides the depth-aware inpainting model to generate and update partial t…
Numerous significant progress on fisheye image rectification has been achieved through CNN. Nevertheless, constrained by a fixed receptive field, the global distribution and the local symmetry of the distortion have not been fully exploited. To leverage these two characteristics, we introduced Fishformer that processes the fisheye image as a sequence to enhance global and local perception. We tuned the Transformer according to the structural properties of fisheye images. First, the uneven distortion distribution in patches generated by the existing square slicing method confuses the network, r…
Arizona State University（亚利桑那州立大学）；Lebanese American University（黎巴嫩美国大学）
Although deep neural networks (DNNs) have been shown to be susceptible to image-agnostic adversarial attacks on natural image classification problems, the effects of such attacks on DNN-based texture recognition have yet to be explored. As part of our work, we find that limiting the perturbation's $l_p$ norm in the spatial domain may not be a suitable way to restrict the perceptibility of universal adversarial perturbations for texture images. Based on the fact that human perception is affected by local visual frequency characteristics, we propose a frequency-tuned universal attack method to c…
xModel-KD: Cross-modal Knowledge Distillation for 3D Scene Perception using LiDAR
Dept. of Computer Science Lakehead University Thunder Bay, Canada；School of Computer Science Engg. \& Info. Systems Vellore Institute of Technology Tamil Nadu, India；Dept. of Software Engg. Lakehead University Thunder Bay, Canada
Ocean University of China（中国海洋大学）；University of Leicester（莱斯特大学）
Textures contain a wealth of image information and are widely used in various fields such as computer graphics and computer vision. With the development of machine learning, the texture synthesis and generation have been greatly improved. As a very common element in everyday life, wallpapers contain a wealth of texture information, making it difficult to annotate with a simple single label. Moreover, wallpaper designers spend significant time to create different styles of wallpaper. For this purpose, this paper proposes to describe wallpaper texture images by using multi-label semantics. Based…
Vision-Driven 2D Supervised Fine-Tuning Framework for Bird's Eye View Perception
School of Vehicle and Mobility, Tsinghua University（清华大学车辆与 Mobility学院）；State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University（清华大学智能绿色车辆与交通全国重点实验室）
Multi-sensor fusion is widely used in the environment perception system of the autonomous vehicle. It solves the interference caused by environmental changes and makes the whole driving system safer and more reliable. In this paper, a novel visible and near-infrared fusion method based on texture information is proposed to enhance unstructured environmental images. It aims at the problems of artifact, information loss and noise in traditional visible and near infrared image fusion methods. Firstly, the structure information of the visible image (RGB) and the near infrared image (NIR) after tex…
Nankai University（南开大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）；Xiamen University（厦门大学）；Cardiff University（卡迪夫大学）；Peng Cheng Lab（鹏城实验室）
Generating high-quality and diverse human images is an important yet challenging task in vision and graphics. However, existing generative models often fall short under the high diversity of clothing shapes and textures. Furthermore, the generation process is even desired to be intuitively controllable for layman users. In this work, we present a text-driven controllable framework, Text2Human, for a high-quality and diverse human generation. We synthesize full-body human images starting from a given human pose with two dedicated steps. 1) With some texts describing the shapes of clothes, the g…
Data-driven perception approaches are well-established in automated driving systems. In many fields even super-human performance is reached. Unlike prediction and planning approaches, mainly supervised learning algorithms are used for the perception domain. Therefore, a major remaining challenge is the efficient generation of ground truth data. As perception modules are positioned close to the sensor, they typically run on raw sensor data of high bandwidth. Due to that, the generation of ground truth labels typically causes a significant manual effort, which leads to high costs for the labelli…
Northwestern University（西北大学）；University of Rochester（罗切斯特大学）
We investigate data-driven texture modeling via analysis and synthesis with generative adversarial networks. For network training and testing, we have compiled a diverse set of spatially homogeneous textures, ranging from stochastic to regular. We adopt StyleGAN3 for synthesis and demonstrate that it produces diverse textures beyond those represented in the training data. For texture analysis, we propose GAN inversion using a novel latent domain reconstruction consistency criterion for synthesized textures, and iterative refinement with Gramian loss for real textures. We propose perceptual pro…
University of Maryland（马里兰大学）；Zhejiang University（浙江大学）；Huzhou Institute of Zhejiang University（浙江大学湖州研究院）；Hong Kong University of Science and Technology（香港科技大学）
Infrared and Visible Image Fusion with Hierarchical Human Perception
School of Electronic Engineering, Xidian University（西安电子科技大学电子工程学院）；Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications（重庆邮电大学图像认知重点实验室）
Surface roughness plays an important role in analyzing engineering surfaces. It quantifies the surface topography and can be used to determine whether the resulting surface finish is acceptable or not. Nevertheless, while several existing tools and standards are available for computing surface roughness, these methods rely heavily on user input thus slowing down the analysis and increasing manufacturing costs. Therefore, fast and automatic determination of the roughness level is essential to avoid costs resulting from surfaces with unacceptable finish, and user-intensive analysis. In this stud…
Xidian University（西安电子科技大学）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
Recently, it has been demonstrated that deep neural networks can significantly improve the performance of single image super-resolution (SISR). Numerous studies have concentrated on raising the quantitative quality of super-resolved (SR) images. However, these methods that target PSNR maximization usually produce blurred images at large upscaling factor. The introduction of generative adversarial networks (GANs) can mitigate this issue and show impressive results with synthetic high-frequency textures. Nevertheless, these GAN-based approaches always have a tendency to add fake textures and eve…
