IEEE Transactions on Visualization and Computer Graphics最新文献_第4页

Local-to-Global Panorama Inpainting for Locale-Aware Indoor Lighting Prediction 局部到全局全景图像绘制用于区域感知室内照明预测

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2023-03-18 DOI: 10.48550/arXiv.2303.10344

Jia-Xuan Bai, Zhen He, Shangxue Yang, Jie Guo, Zhenyu Chen, Y. Zhang, Yanwen Guo

{"title":"Local-to-Global Panorama Inpainting for Locale-Aware Indoor Lighting Prediction","authors":"Jia-Xuan Bai, Zhen He, Shangxue Yang, Jie Guo, Zhenyu Chen, Y. Zhang, Yanwen Guo","doi":"10.48550/arXiv.2303.10344","DOIUrl":"https://doi.org/10.48550/arXiv.2303.10344","url":null,"abstract":"Predicting panoramic indoor lighting from a single perspective image is a fundamental but highly ill-posed problem in computer vision and graphics. To achieve locale-aware and robust prediction, this problem can be decomposed into three sub-tasks: depth-based image warping, panorama inpainting and high-dynamic-range (HDR) reconstruction, among which the success of panorama inpainting plays a key role. Recent methods mostly rely on convolutional neural networks (CNNs) to fill the missing contents in the warped panorama. However, they usually achieve suboptimal performance since the missing contents occupy a very large portion in the panoramic space while CNNs are plagued by limited receptive fields. The spatially-varying distortion in the spherical signals further increases the difficulty for conventional CNNs. To address these issues, we propose a local-to-global strategy for large-scale panorama inpainting. In our method, a depth-guided local inpainting is first applied on the warped panorama to fill small but dense holes. Then, a transformer-based network, dubbed PanoTransformer, is designed to hallucinate reasonable global structures in the large holes. To avoid distortion, we further employ cubemap projection in our design of PanoTransformer. The high-quality panorama recovered at any locale helps us to capture spatially-varying indoor illumination with physically-plausible global structures and fine details.","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2023-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44739180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Topological Distance between Multi-fields based on Multi-Dimensional Persistence Diagrams 基于多维持久化图的多域间拓扑距离

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2023-03-06 DOI: 10.48550/arXiv.2303.03038

Yashwanth Ramamurthi, A. Chattopadhyay

{"title":"A Topological Distance between Multi-fields based on Multi-Dimensional Persistence Diagrams","authors":"Yashwanth Ramamurthi, A. Chattopadhyay","doi":"10.48550/arXiv.2303.03038","DOIUrl":"https://doi.org/10.48550/arXiv.2303.03038","url":null,"abstract":"The problem of computing topological distance between two scalar fields based on Reeb graphs or contour trees has been studied and applied successfully to various problems in topological shape matching, data analysis, and visualization. However, generalizing such results for computing distance measures between two multi-fields based on their Reeb spaces is still in its infancy. Towards this, in the current paper we propose a technique to compute an effective distance measure between two multi-fields by computing a novel multi-dimensional persistence diagram (MDPD) corresponding to each of the (quantized) Reeb spaces. First, we construct a multi-dimensional Reeb graph (MDRG), which is a hierarchical decomposition of the Reeb space into a collection of Reeb graphs. The MDPD corresponding to each MDRG is then computed based on the persistence diagrams of the component Reeb graphs of the MDRG. Our distance measure extends the Wasserstein distance between two persistence diagrams of Reeb graphs to MDPDs of MDRGs. We prove that the proposed measure is a pseudo-metric and satisfies a stability property. Effectiveness of the proposed distance measure has been demonstrated in (i) shape retrieval contest data - SHREC 2010 and (ii) Pt-CO bond detection data from computational chemistry. Experimental results show that the proposed distance measure based on the Reeb spaces has more discriminating power in clustering the shapes and detecting the formation of a stable Pt-CO bond as compared to the similar measures between Reeb graphs.","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45061673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE VR 2023 Message from the Program Chairs and Guest Editors IEEE VR 2023项目主席和客座编辑的信息

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2023-03-01 DOI: 10.1109/tvcg.2021.3067835

Bobby Bodenheimer, V. Popescu, J. Quarles, Lili Wang

引用次数: 0

IntrinsicNGP: Intrinsic Coordinate based Hash Encoding for Human NeRF IntrinsicNGP:基于内在坐标的人类NeRF哈希编码

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2023-02-28 DOI: 10.48550/arXiv.2302.14683

Bo Peng, Jun Hu, Jingtao Zhou, Xuan Gao, Ju-yong Zhang

引用次数: 4

MoReVis: A Visual Summary for Spatiotemporal Moving Regions MoReVis：时空运动区域的可视化总结

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2023-02-26 DOI: 10.48550/arXiv.2302.13199

Giovani Valdrighi, Nivan Ferreira, Jorge Poco

{"title":"MoReVis: A Visual Summary for Spatiotemporal Moving Regions","authors":"Giovani Valdrighi, Nivan Ferreira, Jorge Poco","doi":"10.48550/arXiv.2302.13199","DOIUrl":"https://doi.org/10.48550/arXiv.2302.13199","url":null,"abstract":"Spatial and temporal interactions are central and fundamental in many activities in our world. A common problem faced when visualizing this type of data is how to provide an overview that helps users navigate efficiently. Traditional approaches use coordinated views or 3D metaphors like the Space-time cube to tackle this problem. However, they suffer from overplotting and often lack spatial context, hindering data exploration. More recent techniques, such as MotionRugs, propose compact temporal summaries based on 1D projection. While powerful, these techniques do not support the situation for which the spatial extent of the objects and their intersections is relevant, such as the analysis of surveillance videos or tracking weather storms. In this paper, we propose MoReVis, a visual overview of spatiotemporal data that considers the objects' spatial extent and strives to show spatial interactions among these objects by displaying spatial intersections. Like previous techniques, our method involves projecting the spatial coordinates to 1D to produce compact summaries. However, our solution's core consists of performing a layout optimization step that sets the size and positions of the visual marks on the summary to resemble the actual values on the original space. We also provide multiple interactive mechanisms to make interpreting the results more straightforward for the user. We perform an extensive experimental evaluation and usage scenarios. Moreover, we evaluated the usefulness of MoReVis in a study with 9 participants. The results point out the effectiveness and suitability of our method in representing different datasets compared to traditional techniques.","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2023-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45632088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LC-NeRF: Local Controllable Face Generation in Neural Randiance Field LC-NeRF:神经距离场的局部可控人脸生成

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2023-02-19 DOI: 10.48550/arXiv.2302.09486

Wen-Yang Zhou, Lu Yuan, Shu-Yu Chen, Lin Gao, Shimin Hu

{"title":"LC-NeRF: Local Controllable Face Generation in Neural Randiance Field","authors":"Wen-Yang Zhou, Lu Yuan, Shu-Yu Chen, Lin Gao, Shimin Hu","doi":"10.48550/arXiv.2302.09486","DOIUrl":"https://doi.org/10.48550/arXiv.2302.09486","url":null,"abstract":"3D face generation has achieved high visual quality and 3D consistency thanks to the development of neural radiance fields (NeRF). However, these methods model the whole face as a neural radiance field, which limits the controllability of the local regions. In other words, previous methods struggle to independently control local regions, such as the mouth, nose, and hair. To improve local controllability in NeRF-based face generation, we propose LC-NeRF, which is composed of a Local Region Generators Module (LRGM) and a Spatial-Aware Fusion Module (SAFM), allowing for geometry and texture control of local facial regions. The LRGM models different facial regions as independent neural radiance fields and the SAFM is responsible for merging multiple independent neural radiance fields into a complete representation. Finally, LC-NeRF enables the modification of the latent code associated with each individual generator, thereby allowing precise control over the corresponding local region. Qualitative and quantitative evaluations show that our method provides better local controllability than state-of-the-art 3D-aware face generation methods. A perception study reveals that our method outperforms existing state-of-the-art methods in terms of image quality, face consistency, and editing effects. Furthermore, our method exhibits favorable performance in downstream tasks, including real image editing and text-driven facial image editing.","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2023-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44963218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Audio2Gestures: Generating Diverse Gestures from Audio Audio2Gestures:从音频生成不同的手势

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2023-01-17 DOI: 10.48550/arXiv.2301.06690

Jing Li, Di Kang, Wenjie Pei, Xuefei Zhe, Ying Zhang, Linchao Bao, Zhenyu He

{"title":"Audio2Gestures: Generating Diverse Gestures from Audio","authors":"Jing Li, Di Kang, Wenjie Pei, Xuefei Zhe, Ying Zhang, Linchao Bao, Zhenyu He","doi":"10.48550/arXiv.2301.06690","DOIUrl":"https://doi.org/10.48550/arXiv.2301.06690","url":null,"abstract":"People may perform diverse gestures affected by various mental and physical factors when speaking the same sentences. This inherent one-to-many relationship makes co-speech gesture generation from audio particularly challenging. Conventional CNNs/RNNs assume one-to-one mapping, and thus tend to predict the average of all possible target motions, easily resulting in plain/boring motions during inference. So we propose to explicitly model the one-to-many audio-to-motion mapping by splitting the cross-modal latent code into shared code and motion-specific code. The shared code is expected to be responsible for the motion component that is more correlated to the audio while the motion-specific code is expected to capture diverse motion information that is more independent of the audio. However, splitting the latent code into two parts poses extra training difficulties. Several crucial training losses/strategies, including relaxed motion loss, bicycle constraint, and diversity loss, are designed to better train the VAE. Experiments on both 3D and 2D motion datasets verify that our method generates more realistic and diverse motions than previous state-of-the-art methods, quantitatively and qualitatively. Besides, our formulation is compatible with discrete cosine transformation (DCT) modeling and other popular backbones (i.e. RNN, Transformer). As for motion losses and quantitative motion evaluation, we find structured losses/metrics (e.g. STFT) that consider temporal and/or spatial context complement the most commonly used point-wise losses (e.g. PCK), resulting in better motion dynamics and more nuanced motion details. Finally, we demonstrate that our method can be readily used to generate motion sequences with user-specified motion clips on the timeline.","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2023-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42570159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

NeRF-Art: Text-Driven Neural Radiance Fields Stylization NeRF-Art:文本驱动的神经辐射领域的风格化

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2022-12-15 DOI: 10.48550/arXiv.2212.08070

Can Wang, Ruixia Jiang, Menglei Chai, Mingming He, Dongdong Chen, Jing Liao

{"title":"NeRF-Art: Text-Driven Neural Radiance Fields Stylization","authors":"Can Wang, Ruixia Jiang, Menglei Chai, Mingming He, Dongdong Chen, Jing Liao","doi":"10.48550/arXiv.2212.08070","DOIUrl":"https://doi.org/10.48550/arXiv.2212.08070","url":null,"abstract":"As a powerful representation of 3D scenes, the neural radiance field (NeRF) enables high-quality novel view synthesis from multi-view images. Stylizing NeRF, however, remains challenging, especially in simulating a text-guided style with both the appearance and the geometry altered simultaneously. In this paper, we present NeRF-Art, a text-guided NeRF stylization approach that manipulates the style of a pre-trained NeRF model with a simple text prompt. Unlike previous approaches that either lack sufficient geometry deformations and texture details or require meshes to guide the stylization, our method can shift a 3D scene to the target style characterized by desired geometry and appearance variations without any mesh guidance. This is achieved by introducing a novel global-local contrastive learning strategy, combined with the directional constraint to simultaneously control both the trajectory and the strength of the target style. Moreover, we adopt a weight regularization method to effectively suppress cloudy artifacts and geometry noises which arise easily when the density field is transformed during geometry stylization. Through extensive experiments on various styles, we demonstrate that our method is effective and robust regarding both single-view stylization quality and cross-view consistency. The code and more results can be found on our project page: https://cassiepython.github.io/nerfart/.","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44448914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 34

What's the Situation with Intelligent Mesh Generation: A Survey and Perspectives 智能网格生成的现状与展望

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2022-11-11 DOI: 10.48550/arXiv.2211.06009

Zezeng Li, Zebin Xu, Ying Li, X. Gu, Na Lei

{"title":"What's the Situation with Intelligent Mesh Generation: A Survey and Perspectives","authors":"Zezeng Li, Zebin Xu, Ying Li, X. Gu, Na Lei","doi":"10.48550/arXiv.2211.06009","DOIUrl":"https://doi.org/10.48550/arXiv.2211.06009","url":null,"abstract":"Intelligent Mesh Generation (IMG) represents a novel and promising field of research, utilizing machine learning techniques to generate meshes. Despite its relative infancy, IMG has significantly broadened the adaptability and practicality of mesh generation techniques, delivering numerous breakthroughs and unveiling potential future pathways. However, a noticeable void exists in the contemporary literature concerning comprehensive surveys of IMG methods. This paper endeavors to fill this gap by providing a systematic and thorough survey of the current IMG landscape. With a focus on 113 preliminary IMG methods, we undertake a meticulous analysis from various angles, encompassing core algorithm techniques and their application scope, agent learning objectives, data types, targeted challenges, as well as advantages and limitations. We have curated and categorized the literature, proposing three unique taxonomies based on key techniques, output mesh unit elements, and relevant input data types. This paper also underscores several promising future research directions and challenges in IMG. To augment reader accessibility, a dedicated IMG project page is available at https://github.com/xzb030/IMG_Survey.","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41797284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

GPA-Net: No-Reference Point Cloud Quality Assessment with Multi-task Graph Convolutional Network GPA-Net：基于多任务图卷积网络的无参考点云质量评估

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2022-10-29 DOI: 10.48550/arXiv.2210.16478

Ziyu Shan, Qi Yang, Rui Ye, Yujie Zhang, Yi Xu, Xiaozhong Xu, Shan Liu

{"title":"GPA-Net: No-Reference Point Cloud Quality Assessment with Multi-task Graph Convolutional Network","authors":"Ziyu Shan, Qi Yang, Rui Ye, Yujie Zhang, Yi Xu, Xiaozhong Xu, Shan Liu","doi":"10.48550/arXiv.2210.16478","DOIUrl":"https://doi.org/10.48550/arXiv.2210.16478","url":null,"abstract":"With the rapid development of 3D vision, point cloud has become an increasingly popular 3D visual media content. Due to the irregular structure, point cloud has posed novel challenges to the related research, such as compression, transmission, rendering and quality assessment. In these latest researches, point cloud quality assessment (PCQA) has attracted wide attention due to its significant role in guiding practical applications, especially in many cases where the reference point cloud is unavailable. However, current no-reference metrics which based on prevalent deep neural network have apparent disadvantages. For example, to adapt to the irregular structure of point cloud, they require preprocessing such as voxelization and projection that introduce extra distortions, and the applied grid-kernel networks, such as Convolutional Neural Networks, fail to extract effective distortion-related features. Besides, they rarely consider the various distortion patterns and the philosophy that PCQA should exhibit shift, scaling, and rotation invariance. In this paper, we propose a novel no-reference PCQA metric named the Graph convolutional PCQA network (GPA-Net). To extract effective features for PCQA, we propose a new graph convolution kernel, i.e., GPAConv, which attentively captures the perturbation of structure and texture. Then, we propose the multi-task framework consisting of one main task (quality regression) and two auxiliary tasks (distortion type and degree predictions). Finally, we propose a coordinate normalization module to stabilize the results of GPAConv under shift, scale and rotation transformations. Experimental results on two independent databases show that GPA-Net achieves the best performance compared to the state-of-the-art no-reference PCQA metrics, even better than some full-reference metrics in some cases. The code is available at: https://github.com/Slowhander/GPA-Net.git.","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42738762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5