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arXiv 2024年

作者： Tang, Xiangjun Wu, Linjun Wang, He Wu, Yiqian Hu, Bo Li, Songnan Gong, Xu Liao, Yuchen Kou, Qilong Jin, Xiaogang State Key Lab of CAD&CG Zhejiang University China Department of Computer Science UCL Centre for Artificial Intelligence University College London United Kingdom Tencent Technology Co. Ltd. China State Key Lab of CAD&CG Zhejiang University ZJU-Tencent Game and Intelligent Graphics Innovation Technology Joint Lab China

Motion style transfer changes the style of a motion while retaining its content and is useful in computer animations and games. Contact is an essential component of motion style transfer that should be controlled explicitly in order to express the style vividly while enhancing motion naturalness and quality. However, it is unknown how to decouple and control contact to achieve fine-grained control in motion style transfer. In this paper, we present a novel style transfer method for fine-grained control over contacts while achieving both motion naturalness and spatial-temporal variations of style. Based on our empirical evidence, we propose controlling contact indirectly through the hip velocity, which can be further decomposed into the trajectory and contact timing, respectively. To this end, we propose a new model that explicitly models the correlations between motions and trajectory/contact timing/style, allowing us to decouple and control each separately. Our approach is built around a motion manifold, where hip controls can be easily integrated into a Transformer-based decoder. It is versatile in that it can generate motions directly as well as be used as post-processing for existing methods to improve quality and contact controllability. In addition, we propose a new metric that measures a correlation pattern of motions based on our empirical evidence, aligning well with human perception in terms of motion naturalness. Based on extensive evaluation, our method outperforms existing methods in terms of style expressivity and motion quality. Copyright © 2024, The Authors. All rights reserved.

关键词： computer games

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Empowering smart city situational awareness via big mobile data

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Frontiers of Information Technology & Electronic Engineering 2024年第2期25卷 286-307页

作者： Zhiguang SHAN Lei SHI Bo LI Yanqiang ZHANG Xiatian ZHANG Wei CHEN State Information Center Beijing100045China School of Computer Science and Engineering Beihang UniversityBeijing100191China Zhongguancun Laboratory Beijing100094China Beijing Tendcloud Tianxia Technology Co. Ltd.Beijing100027China The State Key Lab of CAD&CG Zhejiang UniversityHangzhou310027China

Smart city situational awareness has recently emerged as a hot topic in research societies,industries,and governments because of its potential to integrate cutting-edge information technology and solve urgent challenges that modern cities *** example,in the latest five-year plan,the Chinese government has highlighted the demand to empower smart city management with new technologies such as big data and Internet of Things,for which situational awareness is normally the crucial first *** traditional static surveillance data on cities have been available for decades,this review reports a type of relatively new yet highly important urban data source,i.e.,the big mobile data collected by devices with various levels of mobility representing the movement and distribution of public and private agents in the *** especially focus on smart city situational awareness enabled by synthesizing the localization of hundreds of thousands of mobile software Apps using the Global Positioning System(GPS).This technique enjoys advantages such as a large penetration rate(∼50%urban population covered),uniform spatiotemporal coverage,and high localization *** first discuss the pragmatic requirements for smart city situational awareness and the challenges *** we introduce two suites of empowering technologies that help fulfill the requirements of(1)cybersecurity insurance for smart cities and(2)spatiotemporal modeling and visualization for situational awareness,both via big mobile *** main contributions of this review lie in the description of a comprehensive technological framework for smart city situational awareness and the demonstration of its feasibility via real-world applications.

关键词： Smart city Mobile data Situational awareness

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VISHIEN-MAAT:Scrollytelling visualization design for explaining Siamese Neural Network concept to non-technical users

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Visual Informatics 2023年第1期7卷 18-29页

作者： Noptanit Chotisarn Sarun Gulyanon Tianye Zhang Wei Chen The State Key Lab of CAD&CG Zhejiang UniversityHangzhouChina Laboratory of Art and Archaeology Image Zhejiang UniversityMinistry of EducationChina College of Interdisciplinary Studies Thammasat UniversityBangkokThailand

The past decade has witnessed rapid progress in AI research since the breakthrough in deep *** technology has been applied in almost every field;therefore,technical and non-technical endusers must understand these technologies to exploit *** existing materials are designed for experts,but non-technical users need appealing materials that deliver complex ideas in easy-tofollow *** notable tool that fits such a profile is scrollytelling,an approach to storytelling that provides readers with a natural and rich experience at the reader’s pace,along with in-depth interactive explanations of complex ***,this work proposes a novel visualization design for creating a scrollytelling that can effectively explain an AI concept to non-technical *** a demonstration of our design,we created a scrollytelling to explain the Siamese Neural Network for the visual similarity matching *** approach helps create a visualization valuable for a shorttimeline situation like a sales *** results show that the visualization based on our novel design helps improve non-technical users’perception and machine learning concept knowledge acquisition compared to traditional materials like online articles.

关键词： Story synthesis Scrollytelling Visual storytelling Visualizing deep learning Learning science

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NFTVis: Visual Analysis of NFT Performance

NFTVis: Visual Analysis of NFT Performance

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Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Fan Yan Xumeng Wang Ketian Mao Wei Zhang Wei Chen State Key Lab of CAD&CG Zhejiang University College of Computer Science Nankai University Ministry of Education Laboratory of Art and Archaeology Image (Zhejiang University) China

A non-fungible token (NFT) is a data unit stored on the blockchain. Nowadays, more and more investors and collectors (NFT traders), who participate in transactions of NFTs, have an urgent need to assess the performance of NFTs. However, there are two challenges for NFT traders when analyzing the performance of NFT. First, the current rarity models have flaws and are sometimes not convincing. In addition, NFT performance is dependent on multiple factors, such as images (high-dimensional data), history transactions (network), and market evolution (time series). It is difficult to take comprehensive consideration and analyze NFT performance efficiently. To address these challenges, we propose NFTVis, a visual analysis system that facilitates assessing individual NFT performance. A new NFT rarity model is proposed to quantify NFTs with images. Four well-coordinated views are designed to represent the various factors affecting the performance of the NFT. Finally, we evaluate the usefulness and effectiveness of our system using two case studies and user studies.

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Delving into the reversal curse: how far can large language models generalize? 24

Delving into the reversal curse: how far can large language ...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Zhengkai Lin Zhihang Fu Kai Liu Liang Xie Binbin Lin Wenxiao Wang Deng Cai Yue Wu Jieping Ye State Key Lab of CAD & CG Zhejiang University and Alibaba Cloud Alibaba Cloud Alibaba Cloud and College of Biomedical Engineering & Instrument Science Zhejiang University College of Computer Science and Technology Zhejiang University of Technology School of Software Technology Zhejiang University and Fullong Inc. School of Software Technology Zhejiang University State Key Lab of CAD & CG Zhejiang University

ISBN: (纸本)9798331314385

While large language models (LLMs) showcase unprecedented capabilities, they also exhibit certain inherent limitations when facing seemingly trivial tasks. A prime example is the recently debated "reversal curse", which surfaces when models, having been trained on the fact "A is B", struggle to generalize this knowledge to infer that "B is A". In this paper, we examine the manifestation of the reversal curse across various tasks and delve into both the generalization abilities and the problem-solving mechanisms of LLMs. This investigation leads to a series of significant insights: (1) LLMs are able to generalize to "B is A" when both A and B are presented in the context as in the case of a multiple-choice question. (2) This generalization ability is highly correlated to the structure of the fact "A is B" in the training documents. For example, this generalization only applies to biographies structured in "[Name] is [Description]" but not to "[Description] is [Name]". (3) We propose and verify the hypothesis that LLMs possess an inherent bias in fact recalling during knowledge application, which explains and underscores the importance of the document structure to successful learning. (4) The negative impact of this bias on the downstream performance of LLMs can hardly be mitigated through training alone. These findings offer a novel perspective on interpreting LLMs' generalization through their intrinsic mechanisms and provide insights for developing more effective learning methods. https://***/alibaba/thinking_***

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Vox-Fusion++: Voxel-based Neural Implicit Dense Tracking and Mapping with Multi-maps

arXiv

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arXiv 2024年

作者： Zhai, Hongjia Li, Hai Yang, Xingrui Huang, Gan Ming, Yuhang Bao, Hujun Zhang, Guofeng The State Key Lab of CAD&CG Zhejiang University China High-speed Aerodynamics Institute CARDC China The School of Computer Science Hangzhou Dianzi University China

In this paper, we introduce Vox-Fusion++, a multi-maps-based robust dense tracking and mapping system that seamlessly fuses neural implicit representations with traditional volumetric fusion techniques. Building upon the concept of implicit mapping and positioning systems, our approach extends its applicability to real-world scenarios. Our system employs a voxel-based neural implicit surface representation, enabling efficient encoding and optimization of the scene within each voxel. To handle diverse environments without prior knowledge, we incorporate an octree-based structure for scene division and dynamic expansion. To achieve real-time performance, we propose a high-performance multi-process framework. This ensures the system’s suitability for applications with stringent time constraints. Additionally, we adopt the idea of multi-maps to handle large-scale scenes, and leverage loop detection and hierarchical pose optimization strategies to reduce long-term pose drift and remove duplicate geometry. Through comprehensive evaluations, we demonstrate that our method outperforms previous methods in terms of reconstruction quality and accuracy across various scenarios. We also show that our Vox-Fusion++ can be used in augmented reality and collaborative mapping applications. Our source code will be publicly available at https://***/zju3dv/Vox-Fusion_Plus_Plus Copyright © 2024, The Authors. All rights reserved.

关键词： Augmented reality

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Brain-computer control interface design for virtual household appliances based on steady-state visually evoked potential recognition

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Visual Informatics 2020年第1期4卷 1-7页

作者： Fan Zhang Hang Yu Jie Jiang Zhangye Wang Xujia Qin College of Computer Science Zhejiang University of TechnologyChina State Key Lab of CAD&CG Zhejiang UniversityChina

Brain–computer interface is a new form of interaction between humans and *** interaction helps the human brain control or operate external devices directly using electroencephalograph(EEG)*** this study,we first adopt a canonical correlation analysis method to find the stimulation frequency by calculating the correlation coefficient between the EEG data and multiple sets of harmonics with different ***,we select the maximum correlation coefficient as the stimulus frequency and consequently identify steady-state visual evoked ***,we introduce power spectral density to adjust the stimulus frequency and a voting mechanism to reduce the false activation ***,we build a virtual household electrical appliance brain–computer control interface,which achieves over 72.84%accuracy for three classification problems.

关键词： Brain-computer interface Steady-state visually evoked potential Canonical correlation analysis

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Editorial: Guest Editors’ Introduction Special Issue on IEEE PacificVis 2025

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IEEE Transactions on Visualization and computer Graphics 2025年第6期31卷 3677-3678页

作者： Yingcai Wu Melanie Tory Ivan Viola State Key Lab of CAD&CG Zhejiang University Hangzhou China Roux Institute Northeastern University Portland ME USA King Abdullah University of Science and Technology Thuwal Saudi Arabia

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Neural Collapse Inspired Federated Learning with Non-iid Data

arXiv

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arXiv 2023年

作者： Huang, Chenxi Xie, Liang Yang, Yibo Wang, Wenxiao Lin, BinBin Cai, Deng The State Key Laboratory of CAD&CG College of Computer Science Zhejiang University Zhejiang Hangzhou310058 China JD Explore Academy China

One of the challenges in federated learning is the non-independent and identically distributed (non-iid) characteristics between heterogeneous devices, which cause significant differences in local updates and affect the performance of the central server. Although many studies have been proposed to address this challenge, they only focus on local training and aggregation processes to smooth the changes and fail to achieve high performance with deep learning models. Inspired by the phenomenon of neural collapse, we force each client to be optimized toward an optimal global structure for classification. Specifically, we initialize it as a random simplex Equiangular Tight Frame (ETF) and fix it as the unit optimization target of all clients during the local updating. After guaranteeing all clients are learning to converge to the global optimum, we propose to add a global memory vector for each category to remedy the parameter fluctuation caused by the bias of the intra-class condition distribution among clients. Our experimental results show that our method can improve the performance with faster convergence speed on different-size datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Structural optimization

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DiffCalib: Reformulating Monocular Camera Calibration as Diffusion-Based Dense Incident Map Generation 39

DiffCalib: Reformulating Monocular Camera Calibration as Dif...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： He, Xiankang Xu, Guangkai Zhang, Bo Chen, Hao Cui, Ying Guo, Dongyan College of Computer Science and Technology Zhejiang University of Technology China Zhejiang Key Laboratory of Visual Information Intelligent Processing China State Key Lab of CAD & CG Zhejiang University China

ISBN: (纸本)157735897X

Monocular camera calibration is a key precondition for numerous 3D vision applications. Despite considerable advancements, existing methods often hinge on specific assumptions and struggle to generalize across varied real-world scenarios, and the performance is limited by insufficient training data. Recently, diffusion models trained on expansive datasets have been confirmed to maintain the capability to generate diverse, high-quality images. This success suggests a strong potential of the models to effectively understand varied visual information. In this work, we leverage the comprehensive visual knowledge embedded in pre-trained diffusion models to enable more robust and accurate monocular camera intrinsic estimation. Specifically, we reformulate the problem of estimating the four degrees of freedom (4-DoF) of camera intrinsic parameters as a dense incident map generation task. The map details the angle of incidence for each pixel in the RGB image, and its format aligns well with the paradigm of diffusion models. The camera intrinsic can then be derived from the incident map with a simple non-learning RANSAC algorithm during inference. Moreover, to further enhance the performance, we jointly estimate a depth map to provide extra geometric information for the incident map estimation. Extensive experiments on multiple testing datasets demonstrate that our model achieves state-of-the-art performance, gaining up to a 40% reduction in prediction errors. Besides, the experiments also show that the precise camera intrinsic and depth maps estimated by our pipeline can greatly benefit practical applications such as 3D reconstruction from a single in-the-wild image. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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