We propose a Transformer-based framework for 3D human texture estimation from a single image. The proposed Transformer is able to effectively exploit the global information of the input image, overcoming the limitations of existing methods that are solely based on convolutional neural networks. In addition, we also propose a mask-fusion strategy to combine the advantages of the RGB-based and texture-flow-based models. We further introduce a part-style loss to help reconstruct high-fidelity colors without introducing unpleasant artifacts. Extensive experiments demonstrate the effectiveness of the proposed method against state-of-the-art 3D human texture estimation approaches both quantitatively and qualitatively.
VORTEX: Challenging CNNs at Texture Recognition by using Vision Transformers with Orderless and Randomized Token Encodings
São Carlos Institute of Physics, University of São Paulo（圣保罗大学圣卡洛斯物理研究所）；KTH Royal Institute of Technology（瑞典皇家理工学院）；São Paulo State University（圣保罗州立大学）
Microsoft Research（微软研究院）；Shanghai Jiao Tong University（上海交通大学）
We study on image super-resolution (SR), which aims to recover realistic textures from a low-resolution (LR) image. Recent progress has been made by taking high-resolution images as references (Ref), so that relevant textures can be transferred to LR images. However, existing SR approaches neglect to use attention mechanisms to transfer high-resolution (HR) textures from Ref images, which limits these approaches in challenging cases. In this paper, we propose a novel Texture Transformer Network for Image Super-Resolution (TTSR), in which the LR and Ref images are formulated as queries and keys…
SkinFormer: Learning Statistical Texture Representation with Transformer for Skin Lesion Segmentation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Shandong Computer Science Center（山东省计算中心）；Qilu University of Technology（齐鲁工业大学）；Shandong Fundamental Research Center for Computer Science（山东计算机科学基础研究中心）；Beijing University of Posts and Telecommunications（北京邮电大学）
Deep Learning (DL) based methods for magnetic resonance (MR) image reconstruction have been shown to produce superior performance in recent years. However, these methods either only leverage under-sampled data or require a paired fully-sampled auxiliary modality to perform multi-modal reconstruction. Consequently, existing approaches neglect to explore attention mechanisms that can transfer textures from reference fully-sampled data to under-sampled data within a single modality, which limits these approaches in challenging cases. In this paper, we propose a novel Texture Transformer Module (T…
Georgia Institute of Technology（佐治亚理工学院）；Brandon University（布兰登大学）；GN Jabra A/S（GN捷波朗公司）
Hand-held light field cameras suffer from low spatial resolution due to the inherent spatio-angular tradeoff. In this paper, we propose a method to improve the spatial resolution of light field images with the aid of the Texture Transformer Network (TTSR). The proposed method consists of three modules: the first module produces an all-in focus high-resolution perspective image which serves as a reference image for the second module, i.e. TTSR, which in turn produces a high-resolution light field. The last module refines the spatial resolution by imposing a light field prior. The results demons…
City University of Hong Kong（香港城市大学）；Microsoft Cloud + AI（微软云与人工智能部门）
Image completion has made tremendous progress with convolutional neural networks (CNNs), because of their powerful texture modeling capacity. However, due to some inherent properties (e.g., local inductive prior, spatial-invariant kernels), CNNs do not perform well in understanding global structures or naturally support pluralistic completion. Recently, transformers demonstrate their power in modeling the long-term relationship and generating diverse results, but their computation complexity is quadratic to input length, thus hampering the application in processing high-resolution images. This…
A Comparative Survey of Vision Transformers for Feature Extraction in Texture Analysis
São Carlos Institute of Physics, University of São Paulo（圣保罗大学圣卡洛斯物理学院）；Institute of Mathematics and Computer Sciences, University of São Paulo（圣保罗大学数学与计算机科学研究所）；Institute of Biosciences, Humanities and Exact Sciences, São Paulo State University（圣保罗州立大学生物科学、人文与精确科学研究所）；Ghent University（根特大学）
本文系统比较了21种预训练视觉Transformer在纹理特征提取上的表现，发现ViTs在鲁棒性、颜色/材料纹理和属性识别等任务中普遍优于CNNs和手工模型，并指出ViT-B with DINO、BeiTv2、Swin及EfficientFormer为有前景的模型。
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）
Self-supervised monocular depth estimation has been widely studied recently. Most of the work has focused on improving performance on benchmark datasets, such as KITTI, but has offered a few experiments on generalization performance. In this paper, we investigate the backbone networks (e.g. CNNs, Transformers, and CNN-Transformer hybrid models) toward the generalization of monocular depth estimation. We first evaluate state-of-the-art models on diverse public datasets, which have never been seen during the network training. Next, we investigate the effects of texture-biased and shape-biased re…
HiPerViT: A Hierarchical Perceiver-Vision Transformer Architecture for Multi-Scale Texture Recognition
Institute of Mathematics and Computer Sciences (ICMC), University of São Paulo (USP)（圣保罗大学数学与计算机科学研究所）；São Carlos Institute of Physics (IFSC), University of São Paulo (USP)（圣保罗大学圣卡洛斯物理研究所）
VGG-Tex: A Vivid Geometry-Guided Facial Texture Estimation Model for High Fidelity Monocular 3D Face Reconstruction
Renmin University of China（中国人民大学）；Tsinghua University（清华大学）；Beihang University（北京航空航天大学）；Beijing Academy of Blockchain and Edge Computing（北京区块链与边缘计算研究院）
Combining local regularity estimation and total variation optimization for scale-free texture segmentation
Univ Lyon（里昂大学）；Ens de Lyon（里昂高等师范学院）；Univ Lyon 1（里昂第一大学）；CNRS（法国国家科学研究中心）；IRIT（图卢兹计算机研究所）；INP-ENSEEIHT（国立图卢兹应用科学学院-高等电力、电子、计算机、水力与电信学院）；University of Toulouse（图卢兹大学）
Face forgery by deepfake is widely spread over the internet and this raises severe societal concerns. In this paper, we propose a novel video transformer with incremental learning for detecting deepfake videos. To better align the input face images, we use a 3D face reconstruction method to generate UV texture from a single input face image. The aligned face image can also provide pose, eyes blink and mouth movement information that cannot be perceived in the UV texture image, so we use both face images and their UV texture maps to extract the image features. We present an incremental learning…
Estimating 3D human texture from a single image is essential in graphics and vision. It requires learning a mapping function from input images of humans with diverse poses into the parametric (UV) space and reasonably hallucinating invisible parts. To achieve a high-quality 3D human texture estimation, we propose a framework that adaptively samples the input by a deformable convolution where offsets are learned via a deep neural network. Additionally, we describe a novel cycle consistency loss that improves view generalization. We further propose to train our framework with an uncertainty-base…
Technical University of Munich（慕尼黑工业大学）；Google Inc.（谷歌公司）
In this paper, we introduce neural texture learning for 6D object pose estimation from synthetic data and a few unlabelled real images. Our major contribution is a novel learning scheme which removes the drawbacks of previous works, namely the strong dependency on co-modalities or additional refinement. These have been previously necessary to provide training signals for convergence. We formulate such a scheme as two sub-optimisation problems on texture learning and pose learning. We separately learn to predict realistic texture of objects from real image collections and learn pose estimation …
Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection
RWTH Aachen University（亚琛工业大学）；Iran University of Science and Technology（伊朗科技大学）；South Dakota State University（南达科他州立大学）；Shahid Beheshti University（沙希德·贝赫什提大学）；Northwestern University（西北大学）；University of Regensburg（雷根斯堡大学）
Reference-based image super-resolution (RefSR) aims to exploit auxiliary reference (Ref) images to super-resolve low-resolution (LR) images. Recently, RefSR has been attracting great attention as it provides an alternative way to surpass single image SR. However, addressing the RefSR problem has two critical challenges: (i) It is difficult to match the correspondence between LR and Ref images when they are significantly different; (ii) How to transfer the relevant texture from Ref images to compensate the details for LR images is very challenging. To address these issues of RefSR, this paper p…
We introduce VERTEX, an effective solution to recover 3D shape and intrinsic texture of vehicles from uncalibrated monocular input in real-world street environments. To fully utilize the template prior of vehicles, we propose a novel geometry and texture joint representation, based on implicit semantic template mapping. Compared to existing representations which infer 3D texture distribution, our method explicitly constrains the texture distribution on the 2D surface of the template as well as avoids limitations of fixed resolution and topology. Moreover, by fusing the global and local feature…
Hefei University of Technology（合肥工业大学）；Lenovo Research（联想研究院）
Image inpainting has achieved remarkable progress and inspired abundant methods, where the critical bottleneck is identified as how to fulfill the high-frequency structure and low-frequency texture information on the masked regions with semantics. To this end, deep models exhibit powerful superiority to capture them, yet constrained on the local spatial regions. In this paper, we delve globally into texture and structure information to well capture the semantics for image inpainting. As opposed to the existing arts trapped on the independent local patches, the texture information of each patch…
Self-supervised deep learning methods have leveraged stereo images for training monocular depth estimation. Although these methods show strong results on outdoor datasets such as KITTI, they do not match performance of supervised methods on indoor environments with camera rotation. Indoor, rotated scenes are common for less constrained applications and pose problems for two reasons: abundance of low texture regions and increased complexity of depth cues for images under rotation. In an effort to extend self-supervised learning to more generalised environments we propose two additions. First, w…
Improved Prognostic Prediction of Pancreatic Cancer Using Multi-Phase CT by Integrating Neural Distance and Texture-Aware Transformer
DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Peking University（北京大学）；Hupan Lab（湖畔实验室）；Sun Yat-sen University Cancer Center（中山大学肿瘤防治中心）；South China Hospital, Shenzhen University（深圳大学华南医院）；Tianjin Medical University Cancer Institute and Hospital（天津医科大学肿瘤医院）；Changsha Institute for Computing and Digital Economy（长沙计算与数字经济研究院）；Guangdong Provincial People’s Hospital（广东省人民医院）；Shengjing Hospital（盛京医院）
High-Fidelity Facial Albedo Estimation via Texture Quantization
University of Technology Sydney（悉尼科技大学）；Shanghai Jiao Tong University（上海交通大学）；DeepGlint（旷视科技（北京））；Insightface；Zhejiang University（浙江大学）；Imperial College London（伦敦帝国学院）
CUHK-SenseTime Joint Laboratory, The Chinese University of Hong Kong（香港中文大学-商汤科技联合实验室，香港中文大学）；SenseTime Research（商汤科技研究院）；Zhejiang University（浙江大学）；School of CST, Xidian University（西安电子科技大学计算机科学与技术学院）
Video inpainting aims to fill the given spatiotemporal holes with realistic appearance but is still a challenging task even with prosperous deep learning approaches. Recent works introduce the promising Transformer architecture into deep video inpainting and achieve better performance. However, it still suffers from synthesizing blurry texture as well as huge computational cost. Towards this end, we propose a novel Decoupled Spatial-Temporal Transformer (DSTT) for improving video inpainting with exceptional efficiency. Our proposed DSTT disentangles the task of learning spatial-temporal attent…
Institute of Computer Science, FORTH（FORTH 计算机科学研究所）；Computer Science Department, University of Crete（克里特大学计算机科学系）
Vision transformer architectures have been demonstrated to work very effectively for image classification tasks. Efforts to solve more challenging vision tasks with transformers rely on convolutional backbones for feature extraction. In this paper we investigate the use of a pure transformer architecture (i.e., one with no CNN backbone) for the problem of 2D body pose estimation. We evaluate two ViT architectures on the COCO dataset. We demonstrate that using an encoder-decoder transformer architecture yields state of the art results on this estimation problem.
Robust Low-Light Human Pose Estimation through Illumination-Texture Modulation
Nanjing University of Posts and Telecommunications（南京邮电大学）；Surrey Institute for People-Centred Artificial Intelligence（萨里以人为本人工智能研究所）；University of Surrey（萨里大学）
Towards Texture- And Shape-Independent 3D Keypoint Estimation in Birds
Department of Computer and Information Science, University of Konstanz, Germany（康斯坦茨大学计算机与信息科学系）；Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Germany（康斯坦茨大学集体行为高级研究所以）；Department of Collective Behavior, Max Planck Institute of Animal Behavior, Germany（动物行为马克斯·普朗克研究所集体行为系）；Department of Biology, University of Konstanz, Germany（康斯坦茨大学生物学系）
