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From Avatars to Agents: Self-Related Cues Through Embodiment and Personalization Affect Body Perception in Virtual Reality 从头像到代理:通过体现和个性化的自我相关线索影响虚拟现实中的身体感知
Pub Date : 2024-09-13 DOI: 10.1109/TVCG.2024.3456211
Marie Luisa Fielder;Erik Wolf;Nina Döllinger;David Mal;Mario Botsch;Marc Erich Latoschik;Carolin Wienrich
Our work investigates the influence of self-related cues in the design of virtual humans on body perception in virtual reality. In a $2times 2$ mixed design, 64 participants faced photorealistic virtual humans either as a motion-synchronized embodied avatar or as an autonomous moving agent, appearing subsequently with a personalized and generic texture. Our results unveil that self-related cues through embodiment and personalization yield an individual and complemented increase in participants' sense of embodiment and self-identification towards the virtual human. Different body weight modification and estimation tasks further showed an impact of both factors on participants' body weight perception. Additional analyses revealed that the participant's body mass index predicted body weight estimations in all conditions and that participants' self-esteem and body shape concerns correlated with different body weight perception results. Hence, we have demonstrated the occurrence of double standards through induced self-related cues in virtual human perception, especially through embodiment.
我们的研究调查了虚拟人设计中自我相关线索对虚拟现实中身体感知的影响。在一个2美元乘2美元的混合设计中,64名参与者面对的是逼真的虚拟人,要么是运动同步的化身,要么是自主移动的代理,随后出现的是个性化和通用的纹理。我们的研究结果表明,通过化身和个性化的自我相关线索,参与者对虚拟人的化身感和自我认同感得到了个性化和互补性的提高。不同的体重修改和估计任务进一步显示了这两个因素对参与者体重感知的影响。其他分析表明,在所有条件下,参与者的体重指数都能预测体重估算结果,而且参与者的自尊心和对体形的关注与不同的体重感知结果相关。因此,我们证明了在虚拟人感知中通过诱导自我相关线索,特别是通过体现,会出现双重标准。
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引用次数: 0
Searching Across Realities: Investigating ERPs and Eye-Tracking Correlates of Visual Search in Mixed Reality 跨现实搜索:混合现实中视觉搜索的ERP和眼动跟踪相关性研究
Pub Date : 2024-09-12 DOI: 10.1109/TVCG.2024.3456172
Francesco Chiossi;Ines Trautmannsheimer;Changkun Ou;Uwe Gruenefeld;Sven Mayer
Mixed Reality allows us to integrate virtual and physical content into users' environments seamlessly. Yet, how this fusion affects perceptual and cognitive resources and our ability to find virtual or physical objects remains uncertain. Displaying virtual and physical information simultaneously might lead to divided attention and increased visual complexity, impacting users' visual processing, performance, and workload. In a visual search task, we asked participants to locate virtual and physical objects in Augmented Reality and Augmented Virtuality to understand the effects on performance. We evaluated search efficiency and attention allocation for virtual and physical objects using event-related potentials, fixation and saccade metrics, and behavioral measures. We found that users were more efficient in identifying objects in Augmented Virtuality, while virtual objects gained saliency in Augmented Virtuality. This suggests that visual fidelity might increase the perceptual load of the scene. Reduced amplitude in distractor positivity ERP, and fixation patterns supported improved distractor suppression and search efficiency in Augmented Virtuality. We discuss design implications for mixed reality adaptive systems based on physiological inputs for interaction.
混合现实技术让我们能够将虚拟内容和实体内容完美地融入用户的环境中。然而,这种融合如何影响感知和认知资源,以及我们寻找虚拟或物理对象的能力,目前仍不确定。同时显示虚拟和物理信息可能会导致注意力分散和视觉复杂性增加,从而影响用户的视觉处理、表现和工作量。在一项视觉搜索任务中,我们要求参与者在增强现实和增强虚拟中找到虚拟和物理对象,以了解其对性能的影响。我们使用事件相关电位、固定和囊状移动度量以及行为测量来评估虚拟和物理对象的搜索效率和注意力分配。我们发现,用户在增强虚拟现实中识别物体的效率更高,而虚拟物体在增强虚拟现实中的显著性更高。这表明,视觉保真度可能会增加场景的感知负荷。在增强虚拟环境中,分心正向ERP和固定模式的振幅减小,支持了分心抑制和搜索效率的提高。我们讨论了基于生理交互输入的混合现实自适应系统的设计意义。
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引用次数: 0
VPRF: Visual Perceptual Radiance Fields for Foveated Image Synthesis VPRF:用于有焦点图像合成的视觉感知辐射场
Pub Date : 2024-09-11 DOI: 10.1109/TVCG.2024.3456184
Zijun Wang;Jian Wu;Runze Fan;Wei Ke;Lili Wang
Neural radiance fields (NeRF) has achieved revolutionary breakthrough in the novel view synthesis task for complex 3D scenes. However, this new paradigm struggles to meet the requirements for real-time rendering and high perceptual quality in virtual reality. In this paper, we propose VPRF, a novel visual perceptual based radiance fields representation method, which for the first time integrates the visual acuity and contrast sensitivity models of human visual system (HVS) into the radiance field rendering framework. Initially, we encode both the appearance and visual sensitivity information of the scene into our radiance field representation. Then, we propose a visual perceptual sampling strategy, allocating computational resources according to the HVS sensitivity of different regions. Finally, we propose a sampling weight-constrained training scheme to ensure the effectiveness of our sampling strategy and improve the representation of the radiance field based on the scene content. Experimental results demonstrate that our method renders more efficiently, with higher PSNR and SSIM in the foveal and salient regions compared to the state-of-the-art FoV-NeRF. The results of the user study confirm that our rendering results exhibit high-fidelity visual perception.
神经辐射场(NeRF)在复杂三维场景的新颖视图合成任务中取得了革命性的突破。然而,这一新模式难以满足虚拟现实中实时渲染和高感知质量的要求。在本文中,我们提出了基于视觉感知的新型辐射场表示方法 VPRF,首次将人类视觉系统(HVS)的视觉敏锐度和对比敏感度模型集成到辐射场渲染框架中。首先,我们将场景的外观和视觉灵敏度信息编码到我们的辐射场表示法中。然后,我们提出一种视觉感知采样策略,根据不同区域的 HVS 敏感度分配计算资源。最后,我们提出了一种采样权重受限的训练方案,以确保采样策略的有效性,并根据场景内容改进辐射场的表示。实验结果表明,与最先进的 FoV-NeRF 相比,我们的方法渲染效率更高,在眼窝和突出区域具有更高的 PSNR 和 SSIM。用户研究结果证实,我们的渲染结果显示出高保真的视觉感知。
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引用次数: 0
PUREsuggest: Citation-based Literature Search and Visual Exploration with Keyword-controlled Rankings. PUREsuggest:基于引文的文献检索和可视化探索与关键词控制排名。
Pub Date : 2024-09-10 DOI: 10.1109/TVCG.2024.3456199
Fabian Beck

Citations allow quickly identifying related research. If multiple publications are selected as seeds, specifc suggestions for related literature can be made based on the number of incoming and outgoing citation links to this selection. Interactively adding recommended publications to the selection refnes the next suggestion and incrementally builds a relevant collection of publications. Following this approach, the paper presents a search and foraging approach, PUREsuggest, which combines citation-based suggestions with augmented visualizations of the citation network. The focus and novelty of the approach is, frst, the transparency of how the rankings are explained visually and, second, that the process can be steered through user-defned keywords, which refect topics of interests. The system can be used to build new literature collections, to update and assess existing ones, as well as to use the collected literature for identifying relevant experts in the feld. We evaluated the recommendation approach through simulated sessions and performed a user study investigating search strategies and usage patterns supported by the interface.

通过引用可以快速识别相关研究。如果选择了多个出版物作为种子选手,就可以根据与该选题相关的传入和传出引文链接的数量,提出相关文献的具体建议。交互式地将推荐的出版物添加到选区中,可以重新生成下一个建议,并逐步建立相关的出版物集。按照这种方法,本文介绍了一种搜索和觅食方法--PUREsuggest,它将基于引文的建议与引文网络的增强可视化相结合。该方法的重点和新颖之处在于:第一,以可视化方式解释排名的透明度;第二,可以通过用户定义的关键词来引导这一过程,这些关键词反映了用户感兴趣的主题。该系统可用于建立新的文献集,更新和评估现有的文献集,以及利用收集的文献来识别该领域的相关专家。我们通过模拟会话对推荐方法进行了评估,并对界面支持的搜索策略和使用模式进行了用户研究。
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引用次数: 0
CSLens: Towards Better Deploying Charging Stations via Visual Analytics -- A Coupled Networks Perspective. CSLens:通过可视化分析更好地部署充电站--耦合网络的视角。
Pub Date : 2024-09-10 DOI: 10.1109/TVCG.2024.3456392
Yutian Zhang, Liwen Xu, Shaocong Tao, Quanxue Guan, Quan Li, Haipeng Zeng

In recent years, the global adoption of electric vehicles (EVs) has surged, prompting a corresponding rise in the installation of charging stations. This proliferation has underscored the importance of expediting the deployment of charging infrastructure. Both academia and industry have thus devoted to addressing the charging station location problem (CSLP) to streamline this process. However, prevailing algorithms addressing CSLP are hampered by restrictive assumptions and computational overhead, leading to a dearth of comprehensive evaluations in the spatiotemporal dimensions. Consequently, their practical viability is restricted. Moreover, the placement of charging stations exerts a significant impact on both the road network and the power grid, which necessitates the evaluation of the potential post-deployment impacts on these interconnected networks holistically. In this study, we propose CSLens, a visual analytics system designed to inform charging station deployment decisions through the lens of coupled transportation and power networks. CSLens offers multiple visualizations and interactive features, empowering users to delve into the existing charging station layout, explore alternative deployment solutions, and assess the ensuring impact. To validate the efficacy of CSLens, we conducted two case studies and engaged in interviews with domain experts. Through these efforts, we substantiated the usability and practical utility of CSLens in enhancing the decision-making process surrounding charging station deployment. Our findings underscore CSLens's potential to serve as a valuable asset in navigating the complexities of charging infrastructure planning.

近年来,全球电动汽车(EV)的采用率激增,促使充电站的安装量也相应增加。这种激增凸显了加快充电基础设施部署的重要性。因此,学术界和工业界都致力于解决充电站位置问题(CSLP),以简化这一过程。然而,解决 CSLP 问题的现有算法受到了限制性假设和计算开销的阻碍,导致缺乏对时空维度的全面评估。因此,这些算法的实际可行性受到了限制。此外,充电站的布置会对道路网络和电网产生重大影响,因此有必要全面评估其部署后对这些互连网络的潜在影响。在本研究中,我们提出了 CSLens,这是一个可视化分析系统,旨在从交通和电力网络耦合的角度为充电站部署决策提供信息。CSLens 提供多种可视化和互动功能,使用户能够深入了解现有充电站布局,探索替代部署方案,并评估其影响。为了验证 CSLens 的功效,我们进行了两项案例研究,并与领域专家进行了访谈。通过这些努力,我们证实了 CSLens 在加强充电站部署决策过程中的可用性和实用性。我们的研究结果强调了 CSLens 在应对充电基础设施规划的复杂性方面作为宝贵资产的潜力。
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引用次数: 0
Tasks Reflected in the Eyes: Egocentric Gaze-Aware Visual Task Type Recognition in Virtual Reality 反映在眼睛中的任务:虚拟现实中以自我为中心的凝视感知视觉任务类型识别。
Pub Date : 2024-09-10 DOI: 10.1109/TVCG.2024.3456164
Zhimin Wang;Feng Lu
With eye tracking finding widespread utility in augmented reality and virtual reality headsets, eye gaze has the potential to recognize users' visual tasks and adaptively adjust virtual content displays, thereby enhancing the intelligence of these headsets. However, current studies on visual task recognition often focus on scene-specific tasks, like copying tasks for office environments, which lack applicability to new scenarios, e.g., museums. In this paper, we propose four scene-agnostic task types for facilitating task type recognition across a broader range of scenarios. We present a new dataset that includes eye and head movement data recorded from 20 participants while they engaged in four task types across 15 360-degree VR videos. Using this dataset, we propose an egocentric gaze-aware task type recognition method, TRCLP, which achieves promising results. Additionally, we illustrate the practical applications of task type recognition with three examples. Our work offers valuable insights for content developers in designing task-aware intelligent applications. Our dataset and source code are available at zhimin-wang.github.io/TaskTypeRecognition.html.
随着眼球跟踪技术在增强现实和虚拟现实头戴设备中的广泛应用,眼球凝视技术有望识别用户的视觉任务,并自适应地调整虚拟内容显示,从而提高这些头戴设备的智能化程度。然而,目前有关视觉任务识别的研究往往侧重于特定场景的任务,如办公室环境中的复印任务,缺乏对博物馆等新场景的适用性。在本文中,我们提出了四种与场景无关的任务类型,以便在更广泛的场景中促进任务类型的识别。我们展示了一个新的数据集,其中包括 20 名参与者在 15 个 360 度 VR 视频中参与四种任务类型时记录的眼球和头部运动数据。利用该数据集,我们提出了一种以自我为中心的凝视感知任务类型识别方法 TRCLP,该方法取得了可喜的成果。此外,我们还通过三个实例说明了任务类型识别的实际应用。我们的工作为内容开发人员设计任务感知智能应用程序提供了宝贵的见解。我们的数据集和源代码见 zhimin-wang.github.io/TaskTypeRecognition.html。
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引用次数: 0
Frankenstein's Monster in the Metaverse: User Interaction With Customized Virtual Agents 元宇宙中的科学怪人:用户与定制虚拟代理的互动
Pub Date : 2024-09-10 DOI: 10.1109/TVCG.2024.3456205
Susanne Schmidt;Ipek Köysürenbars;Frank Steinicke
Enabled by the latest achievements in artificial intelligence (AI), computer graphics as well as virtual, augmented, and mixed reality (VR/AR/MR), virtual agents are increasingly resembling humans in both their appearance and intelligent behavior. This results in enormous potential for agents to support users in their daily lives, for example in customer service, healthcare, education or the envisioned all-encompassing metaverse. Today's technology would allow users to customize their conversation partners in the metaverse - as opposed to reality - according to their preferences, potentially improving the user experience. On the other hand, there is little research on how reshaping the head of a communication partner might affect the immediate interaction with them. In this paper, we investigate the user requirements for and the effects of agent customization. In a two-stage user study ($N=30$), we collected both self-reported evaluations (e.g., intrinsic motivation) and interaction metrics (e.g., interaction duration and number of tried out items) for the process of agent customization itself as well as data on how users perceived the subsequent human-agent interaction in VR. Our results indicate that users only wish to have full customization for agents in their personal social circle, while for general services, a selection or even a definite assignment of pre-configured agents is sufficient. When customization is offered, attributes such as gender, clothing or hair are subjectively more relevant to users than facial features such as skin or eye color. Although the customization of human interaction partners is beyond our control, customization of virtual agents significantly increases perceived social presence as well as rapport and trust. Further findings on user motivation and agent diversity are discussed in the paper.
在人工智能(AI)、计算机图形学以及虚拟现实、增强现实和混合现实(VR/AR/MR)等领域最新成果的推动下,虚拟代理在外观和智能行为上都越来越像人类。这使得虚拟代理在日常生活中为用户提供支持的潜力巨大,例如在客户服务、医疗保健、教育或设想中的包罗万象的元宇宙中。如今的技术可以让用户根据自己的喜好,在元宇宙(与现实相反)中定制他们的对话伙伴,从而改善用户体验。另一方面,关于重塑交流伙伴的头像会如何影响与他们的即时互动的研究却很少。在本文中,我们研究了用户对代理定制的需求及其影响。在一项分两个阶段进行的用户研究($N=30$)中,我们收集了用户对代理定制过程本身的自我报告评价(如内在动机)和交互指标(如交互持续时间和尝试项目数量),以及用户对随后在 VR 中进行的人机交互的感知数据。我们的研究结果表明,用户只希望对其个人社交圈中的代理进行完全定制,而对于一般服务,选择甚至确定分配预先配置好的代理就足够了。在提供定制服务时,性别、服装或头发等属性比皮肤或眼睛颜色等面部特征与用户的主观相关性更大。虽然人类交互伙伴的定制不是我们所能控制的,但虚拟代理的定制却能显著提高感知到的社交存在感以及融洽度和信任度。本文还讨论了有关用户动机和代理多样性的进一步研究结果。
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引用次数: 0
VisEval: A Benchmark for Data Visualization in the Era of Large Language Models. VisEval:大型语言模型时代的数据可视化基准。
Pub Date : 2024-09-10 DOI: 10.1109/TVCG.2024.3456320
Nan Chen, Yuge Zhang, Jiahang Xu, Kan Ren, Yuqing Yang

Translating natural language to visualization (NL2VIS) has shown great promise for visual data analysis, but it remains a challenging task that requires multiple low-level implementations, such as natural language processing and visualization design. Recent advancements in pre-trained large language models (LLMs) are opening new avenues for generating visualizations from natural language. However, the lack of a comprehensive and reliable benchmark hinders our understanding of LLMs' capabilities in visualization generation. In this paper, we address this gap by proposing a new NL2VIS benchmark called VisEval. Firstly, we introduce a high-quality and large-scale dataset. This dataset includes 2,524 representative queries covering 146 databases, paired with accurately labeled ground truths. Secondly, we advocate for a comprehensive automated evaluation methodology covering multiple dimensions, including validity, legality, and readability. By systematically scanning for potential issues with a number of heterogeneous checkers, VisEval provides reliable and trustworthy evaluation outcomes. We run VisEval on a series of state-of-the-art LLMs. Our evaluation reveals prevalent challenges and delivers essential insights for future advancements.

将自然语言转化为可视化(NL2VIS)在可视化数据分析方面大有可为,但这仍然是一项具有挑战性的任务,需要多种底层实现,如自然语言处理和可视化设计。预训练大型语言模型(LLM)的最新进展为从自然语言生成可视化开辟了新途径。然而,由于缺乏全面可靠的基准,阻碍了我们对 LLM 在可视化生成方面能力的了解。在本文中,我们提出了一种名为 VisEval 的新 NL2VIS 基准,从而弥补了这一空白。首先,我们引入了一个高质量、大规模的数据集。该数据集包括覆盖 146 个数据库的 2524 个具有代表性的查询,并与精确标注的地面真实数据配对。其次,我们主张采用全面的自动评估方法,涵盖多个维度,包括有效性、合法性和可读性。通过使用大量异构检查器系统地扫描潜在问题,VisEval 可以提供可靠、可信的评估结果。我们在一系列最先进的 LLM 上运行 VisEval。我们的评估揭示了普遍存在的挑战,并为未来的进步提供了重要启示。
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引用次数: 0
The Effects of Depth of Knowledge of a Virtual Agent 虚拟代理知识深度的影响
Pub Date : 2024-09-10 DOI: 10.1109/TVCG.2024.3456148
Fu-Chia Yang;Kevin Duque;Christos Mousas
We explored the impact of depth of knowledge on conversational agents and human perceptions in a virtual reality (VR) environment. We designed experimental conditions with low, medium, and high depths of knowledge in the domain of game development and tested them among 27 game development students. We aimed to understand how the agent's predefined knowledge levels affected the participants' perceptions of the agent and its knowledge. Our findings showed that participants could distinguish between different knowledge levels of the virtual agent. Moreover, the agent's depth of knowledge significantly impacted participants' perceptions of intelligence, rapport, factuality, the uncanny valley effect, anthropomorphism, and willingness for future interaction. We also found strong correlations between perceived knowledge, perceived intelligence, factuality, and willingness for future interactions. We developed design guidelines for creating conversational agents from our data and observations. This study contributes to the human-agent interaction field in VR settings by providing empirical evidence on the importance of tailoring virtual agents' depth of knowledge to improve user experience, offering insights into designing more engaging and effective conversational agents.
我们探索了知识深度对虚拟现实(VR)环境中对话代理和人类感知的影响。我们在游戏开发领域设计了低、中、高知识深度的实验条件,并在 27 名游戏开发专业的学生中进行了测试。我们旨在了解代理的预定知识水平如何影响参与者对代理及其知识的感知。我们的研究结果表明,参与者能够区分虚拟代理的不同知识水平。此外,代理的知识深度极大地影响了参与者对智能、亲和力、事实性、不可思议谷效应、拟人化和未来互动意愿的感知。我们还发现,感知知识、感知智力、事实性和未来互动意愿之间存在很强的相关性。我们根据数据和观察结果制定了创建对话式代理的设计指南。这项研究为虚拟现实环境中的人机交互领域做出了贡献,它提供了实证证据,证明了定制虚拟代理的知识深度对改善用户体验的重要性,为设计更具吸引力和更有效的对话代理提供了启示。
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引用次数: 0
Smartboard: Visual Exploration of Team Tactics with LLM Agent. 智能板:利用 LLM Agent 对团队战术进行可视化探索。
Pub Date : 2024-09-10 DOI: 10.1109/TVCG.2024.3456200
Ziao Liu, Xiao Xie, Moqi He, Wenshuo Zhao, Yihong Wu, Liqi Cheng, Hui Zhang, Yingcai Wu

Tactics play an important role in team sports by guiding how players interact on the field. Both sports fans and experts have a demand for analyzing sports tactics. Existing approaches allow users to visually perceive the multivariate tactical effects. However, these approaches require users to experience a complex reasoning process to connect the multiple interactions within each tactic to the final tactical effect. In this work, we collaborate with basketball experts and propose a progressive approach to help users gain a deeper understanding of how each tactic works and customize tactics on demand. Users can progressively sketch on a tactic board, and a coach agent will simulate the possible actions in each step and present the simulation to users with facet visualizations. We develop an extensible framework that integrates large language models (LLMs) and visualizations to help users communicate with the coach agent with multimodal inputs. Based on the framework, we design and develop Smartboard, an agent-based interactive visualization system for fine-grained tactical analysis, especially for play design. Smartboard provides users with a structured process of setup, simulation, and evolution, allowing for iterative exploration of tactics based on specific personalized scenarios. We conduct case studies based on real-world basketball datasets to demonstrate the effectiveness and usefulness of our system.

战术在团队运动中发挥着重要作用,它指导着球员在场上的互动方式。体育迷和专家都需要对体育战术进行分析。现有的方法允许用户直观地感知多元战术效果。然而,这些方法需要用户经历复杂的推理过程,才能将每个战术中的多重互动与最终战术效果联系起来。在这项工作中,我们与篮球专家合作,提出了一种循序渐进的方法,帮助用户深入了解每种战术的作用,并按需定制战术。用户可以在战术板上逐步绘制草图,教练代理将模拟每个步骤中可能出现的动作,并通过切面可视化将模拟结果呈现给用户。我们开发了一个可扩展的框架,将大型语言模型(LLM)和可视化整合在一起,帮助用户通过多模态输入与教练代理交流。基于该框架,我们设计并开发了基于代理的交互式可视化系统 Smartboard,用于精细战术分析,尤其是战术设计。Smartboard 为用户提供了一个结构化的设置、模拟和演化过程,允许用户根据特定的个性化场景对战术进行迭代探索。我们基于真实世界的篮球数据集进行了案例研究,以证明我们系统的有效性和实用性。
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引用次数: 0
期刊
IEEE transactions on visualization and computer graphics
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