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A Comprehensive Review of Redirected Walking Techniques:Taxonomy,Methods,and Future Directions

Journal of Computer Science & technology 2022年第3期37卷 561-583页

作者： Yi-Jun Li Frank Steinicke Miao Wang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Department of Informatics Universität HamburgHamburg 20146Germany Peng Cheng Laboratory Shenzhen 518055China

virtual reality(VR)allows users to explore and experience a computer-simulated virtual environment so that VR users can be immersed in a totally artificial virtual world and interact with arbitrary virtual ***,the limited physical tracking space usually restricts the exploration of large virtual spaces,and VR users have to use special locomotion techniques to move from one location to *** these techniques,redirected walking(RDW)is one of the most natural locomotion techniques to solve the problem based on near-natural walking *** core idea of the RDW technique is to imperceptibly guide users on virtual paths,which might vary from the paths they physically walk in the real *** a similar way,some RDW algorithms imperceptibly change the structure and layout of the virtual environment such that the virtual environment fits into the tracking *** this survey,we first present a taxonomy of existing RDW *** on this taxonomy,we compare and analyze both contributions and shortcomings of the existing methods in detail,and find view manipulation methods offer satisfactory visual effect but the experience can be interrupted when users reach the physical boundaries,while virtual environment manipulation methods can provide users with consistent movement but have limited application ***,we discuss possible future research directions,indicating combining artificial intelligence with this area will be effective and intriguing.

关键词： virtual reality locomotion redirected walking

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Generative Cooperative Network for Person Image Generation 6

Generative Cooperative Network for Person Image Generation

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6th IEEE International Conference on Universal Village, UV 2022

作者： Liu, Yang Wang, Shuai Wu, Yubin Sheng, Hao Xiong, Zhang Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing China

ISBN: (纸本)9781665474771

Person image generation is to synthesize realistic pedestrian images that follow the same distribution as the given dataset. Previous attempts can be generally categorized into two classes: some methods use human pose information as guidance and others try to generate person images from scratch. The former is to transfer the pose of a source image to a reference pose. The generated person image have the same identity as the source image. The latter takes a random noise from latent space as input, and the real person images are only used as references for the discriminator. While pose-guided person image generation is widely studied, generating-from-scratch methods are also worth exploring because they can synthesize person image with new identity, which is a useful manner of data augmentation. These two types of generating methods have their different advantages and disadvantages, and sometimes they are complementary. In this work, the authors design a Generative Cooperative Network (GCN) to jointly train two types of GANs. The two GANs serve different purposes, and can learn from each other during the cooperative learning procedure. The proposed approach is verified on public datasets, and the results show that our GCN improves the performance of the baseline methods. Comparisons with state-of-the-art methods also prove the effectiveness of the proposed method. © 2022 IEEE.

关键词： Computer vision

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Dense object detection for remote sensing images based on multi-scale partitioning and super-resolution optimization 24

Dense object detection for remote sensing images based on mu...

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3rd International Conference on Signal Processing, Computer Networks and Communications, SPCNC 2024

作者： Li, Lu Zhu, Xia Ni, Shaofeng Gao, Fan State Grid Location based Service Co. Ltd. Beijing China State Grid Information & Telecommunication Group Co. Ltd. Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China

ISBN: (纸本)9798400710834

Detecting dense objects in remote sensing image is a critical yet challenging task due to variations in target scale, image resolution, and complex backgrounds. Traditional detection methods often struggle with these dense regions, particularly for small or overlapping objects. To overcome these limitations, this study introduces an innovative approach that combines multi-scale partitioning with super-resolution optimization to improve the detection of densely packed objects. By applying K-Means clustering to identify regions of interest (ROI) and using a local super-resolution network to enhance these areas, we improve the clarity and detectability of small-scale objects. Additionally, we implement a global-local feature extraction architecture, where global features are captured using Transformer-based attention, while local features focus on fine-grained object details. Non-Maximum Suppression (NMS) is employed to fuse multi-scale features and eliminate redundant detections. Experimental results on the DOTA and HRSC2016 datasets demonstrate the effectiveness of our approach, achieving superior performance over state-of-the-art methods in detecting small and densely packed targets. This method shows significant promise for remote sensing applications requiring high-precision target detection. © 2024 Copyright held by the owner/author(s).

关键词： Proximity sensors

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Language Embedded 3D Gaussians for Open-Vocabulary Scene Understanding

Language Embedded 3D Gaussians for Open-Vocabulary Scene Und...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jin-Chuan Shi Miao Wang Hao-Bin Duan Shao-Hua Guan State Key Laboratory of Virtual Reality Technology and Systems SCSE Beihang University Zhongguancun Laboratory

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Open-vocabulary querying in 3D space is challenging but essential for scene understanding tasks such as ob-ject localization and segmentation. Language-embedded scene representations have made progress by incorporating language features into 3D spaces. However, their effi-cacy heavily depends on neural networks that are resource-intensive in training and rendering. Although recent 3D Gaussians offer efficient and high-quality novel view syn-thesis, directly embedding language features in them leads to prohibitive memory usage and decreased performance. In this work, we introduce Language Embedded 3D Gaus-sians, a novel scene representation for open-vocabulary query tasks. Instead of embedding high-dimensional raw semantic features on 3D Gaussians, we propose a dedicated quantization scheme that drastically alleviates the mem-ory requirement, and a novel embedding procedure that achieves smoother yet high accuracy query, countering the multi-view feature inconsistencies and the high-frequency inductive bias in point-based representations. Our compre-hensive experiments show that our representation achieves the best visual quality and language querying accuracy across current language-embedded representations, while maintaining real-time rendering frame rates on a single desktop GPU. Project page: https://***/LEGaussians/.

关键词： Training Visualization Three-dimensional displays Accuracy Uncertainty TV Vector quantization

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SL-SFGR: Segmentation Learning Coupling Spatial-Frequency Structure Information Enhancement for Guiding Registration

SL-SFGR: Segmentation Learning Coupling Spatial-Frequency St...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Gao, Fan He, Yuanbo Li, Shuai Hao, Aimin Wang, Meiyun Cao, Desen Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing China Henan Academy of Innovations in Medical Science Zhengzhou China Chinese Pla General Hospital Department of Biomedical Engineering Beijing China

ISBN: (纸本)9798350386226

The core of medical image registration lies in the alignment of corresponding structures. Hence, the effective construction of structure information in images is crucial for guiding registration. Some methods directly introduce explicit structure priors for assisting registration training, but obtaining high-precision structure priors is intrinsically challenging. As a segmentation model for obtaining structure priors, its capability is built upon the effective construction of structure information, thus its model learning can also be used to guide registration. However, existing registration-segmentation joint methods mostly only use segmentation results at the output level to constrain registration, neglecting the guidance of segmentation learning at the feature level for registration. Moreover, most existing methods only extract features in the image spatial domain for registration, overlooking the structure information in the frequency domain that is more easily captured to guide registration. To this end, this paper proposes an innovative registration method, namely Segmentation Learning Coupling Spatial-Frequency Structure Information Enhancement for Guiding Registration (SL-SFGR). Specifically, first, a semi-supervised segmentation learning network is constructed based on the registration deformation field to introduce structure features suitable for guiding registration. Second, an adaptive feature enhancement module is built in the spatial-frequency dual domain to further strengthen the inherent structure features. Finally, Dynamic Weight Average (DWA) is utilized for joint optimization of the model. The effectiveness of the proposed method has been verified on different brain MRI datasets. The related code is available at: https://***/goghfan/SL-SFGR/. © 2024 IEEE.

关键词： Frequency domain analysis

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LogicST: A Logical Self-Training Framework for Document-Level Relation Extraction with Incomplete Annotations

LogicST: A Logical Self-Training Framework for Document-Leve...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Fan, Shengda Wang, Yanting Mo, Shasha Niu, Jianwei School of Cyber Science and Technology Beihang University Beijing100191 China Zhongguancun Laboratory Beijing100191 China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9798891761643

Document-level relation extraction (DocRE) aims to identify relationships between entities within a document. Due to the vast number of entity pairs, fully annotating all fact triplets is challenging, resulting in datasets with numerous false negative samples. Recently, self-training-based methods have been introduced to address this issue. However, these methods are purely black-box and sub-symbolic, making them difficult to interpret and prone to overlooking symbolic interdependencies between relations. To remedy this deficiency, our insight is that symbolic knowledge, such as logical rules, can be used as diagnostic tools to identify conflicts between pseudo-labels. By resolving these conflicts through logical diagnoses, we can correct erroneous pseudo-labels, thus enhancing the training of neural models. To achieve this, we propose LogicST, a neural-logic self-training framework that iteratively resolves conflicts and constructs the minimal diagnostic set for updating models. Extensive experiments demonstrate that LogicST significantly improves performance and outperforms previous state-of-the-art methods. For instance, LogicST achieves an increase of 7.94% in F1 score compared to CAST (Tan et al., 2023a) on the DocRED benchmark (Yao et al., 2019). Additionally, LogicST is more time-efficient than its self-training counterparts, requiring only 10% of the training time of CAST. Code is available at https://***/XingYing-stack/LogicST. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Multi-Task Feature Decomposition Based Marginal Distribution for Person Search

Multi-Task Feature Decomposition Based Marginal Distribution...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Yang, Yuanzhe Zhang, Xin Geng, Qichuan Chu, Chengxiang Zhou, Zhong Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing China Capital Normal University Beijing China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Person search is a composite task, aiming at locating and identifying a query person from uncropped images. It requires jointly solving Pedestrian Detection and Person Re-identification. One major challenge in person search is the contradictory goals of detection and re-identification. The model has to simultaneously model the universality and specificity of persons. In this paper, we propose a novel parameter-free approach called Feature Decomposition Person Search (FDPS) to separate various tasks. FDPS decomposes the ROI feature map to extract sub-features based on the marginal distribution for different tasks. Also, we find that the Online Instance Match loss pays imbalanced attention to positive and negative categories. We present a Balance Online Instance Match (BOIM) loss to enhance the contribution of negative categories during training. Our method achieves the state-of-the-art performance in one-step methods on two prevailing benchmarks, with high efficiency. © 2022 IEEE.

关键词： Benchmarking

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Exploring the Overall Stiffness Distribution Law of Weakly Rigid Composite Fairings 7th

Exploring the Overall Stiffness Distribution Law of Weakly R...

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7th China Aeronautical Science and technology Conference, CASTC 2024

作者： Yin, Rongying Kong, Chuifeng Dong, Yushuang Ma, Jichuan Gao, Hanjun Harbin Hafei Aviation Industry Co Ltd Harbin150060 China School of Mechanical Engineering and Automation State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China School of Technology Forestry University Beijing100083 China

ISBN: (纸本)9789819797677

To elucidate the stresses and deformations occurring during the assembly of the powerhouse fairing, finite element analysis (ABAQUS) was employed to emulate the assembly process. A numerical simulation model was constructed for the assembly process of the weakly rigid composite fairing by digitizing its geometry, material properties, and the forces exerted during assembly. Initially, the geometric data and material parameters of the fairing were meticulously collected and analyzed, considering its weakly rigid composite material properties. Subsequently, the finite element analysis method was utilized to partition the fairing’s geometry into minute regions, thereby establishing a numerical simulation model of the fairing assembly process. During the simulation, various forces acting on the fairing during assembly, such as the binding force of the assembly fixture and mechanical loads, were taken into account. The stress and deformation distributions during the fairing assembly process were derived through numerical simulation of the model. The findings indicate that the overall stiffness distribution law of the weakly rigid composite fairing can be summarized as follows: larger fixed boundaries, smaller applied forces, more rigid materials, and more intricate geometries enhance the overall stiffness of the fairing. © Chinese Society of Aeronautics and Astronautics 2025.

关键词： ABAQUS

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Tracking dynamic flow: Decoding flow fluctuations through performance in a fine motor control task

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IEEE Transactions on Affective Computing 2024年第2期16卷 891-902页

作者： Tian, Bohao Zhang, Shijun Chen, Sirui Zhang, Yuru Peng, Kaiping Zhang, Hongxing Wang, Dangxiao Tsinghua University Department of Psychology Beijing100084 China Beijing Institute of Life Omics Beijing102206 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Beihang University School of Mechanical Engineering and Automation Beijing100191 China Peng Cheng Laboratory Shenzhen518000 China

Flow, an optimal mental state merging action and awareness, significantly impacts our emotion, performance, and well-being. However, capturing its swift transitions on a fine timescale is challenging due to the sparsity of the existing flow detecting tools. Here we present a fine fingertip force control (F3C) task to induce flow, wherein the task challenge is set at a compatible level with personal skill, and to quantitatively track the flow state variations from synchronous motor control performance. We select eight performance metrics from fingertip force sequence and reveal their significant differences under distinct self-reported flow states. Further, we built a machine learning-based decoder that aims to predict the continuous flow intensity during the user experiment through the performance metrics, taking the self-reported flow as the label. Cross-validation shows that the predicted flow intensity reaches significant correlation with the self-reported flow intensity (r = 0.81). Based on the decoding results, we can capture the flow fluctuations during the intervals between sparse self-reporting probes. This study showcases the feasibility of tracking intrinsic flow variations with high temporal resolution using task performance measures and may serve as foundation for future work aiming to take advantage of flow's dynamics to enhance performance and positive emotions. © 2010-2012 IEEE.

关键词： Force Measurement Force Control Fluctuations Decoding Pressing Dynamics Affective Computing Transducers Real Time systems Flow Experience Intrinsic Fluctuations Fine Fingertip Force Control Task Performance Cross Validation Dynamics Task Performance Control Task Flow Dynamics Fine Control Flow Fluctuations Fine Motor Control Tasks Performance Metrics Volume Flow Flow Variables Personal Skills Force Control Finer Time Scales Synchronization Performance Paired T Test Prediction Error Cohens D Flow Meter Task Difficulty Target Range Random Permutations Power Spectral Density Analysis Flow Experience Experience Sampling Method Flow Probe Main Session Force Output Subjective Metrics Decoding Performance Target Force Decoding Accuracy

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Crossing Narrative: Exploring the Possibilities of Crossing the virtuality and reality in Interactive Narrative Experiences 23

Crossing Narrative: Exploring the Possibilities of Crossing ...

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2023 SIGGRAPH Asia Posters, SA 2023

作者： Liu, Zixiao Yan, Shuo Shen, Xukun School of New Media Art and Design Beihang University China School of New Media Art and Design State Key Laboratory of Virtual Reality Technology and Systems Beihang University China

ISBN: (纸本)9798400703133

With the development of mixed reality technology, the convergence of virtual and real experiences has emerged as a trend in the interactive narratives. In this paper, we introduce "Crossing Narrative", an interactive narrative experience that seamlessly blends virtuality and reality by utilizing real-world views and bystanders. We discuss specific methods for designing cross-reality narrative experience, focusing on three key aspects of cross-reality interactions: diegetic objects, transition effects, and bystander’s avatar. Our design methods aim to enhance audience social interactions and understanding of the immersive narrative. © 2023 Copyright held by the owner/author(s).

关键词： Mixed reality

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