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Introducing Agent Personality in Crowd Simulation Improves Social Presence and Experienced Realism in Immersive VR

IEEE Transactions on Visualization and computer Graphics 2025年第5期31卷 3354-3364页

作者： Pascoli, Massimiliano Buttussi, Fabio Schekotihin, Konstantin Chittaro, Luca University of Udine Human-Computer Interaction Lab Italy University of Klagenfurt Austria

Convincing crowd behavior simulation is becoming essential in many application domains, including video games, cinematography, urban planning, safety simulations, and training. In this paper, we propose a novel and lightweight mesoscopic system for personality-based crowd simulation in immersive virtual reality (iVR). We use the Big Five personality framework, also known as OCEAN, to model a synthetic personality for each autonomous agent. Agents can autonomously aggregate in formations using machine learning-based clustering techniques operating on OCEAN. Moreover, agents can also externalize their personality traits by performing peculiar behavioral animations. To choose which animations to perform, we adopt a probabilistic approach that considers each OCEAN dimension as a continuous spectrum with two extremes linked to pairs of animations. Our system is designed to be flexible and suitable for different applications. Flexibility is achieved by using graphs to store agent and map topology data that control how the agents move and behave at runtime. In a within-subjects study with 40 users, we compare our personality-based system against a basic system that does not use personality. Results show that introducing personality into iVR crowd simulation enhances users' social presence and experienced realism. Introducing personality also increases the perceived match between the agents and the virtual environment where the simulation takes place. © 2025 IEEE.

关键词： Animation

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Advancing Handwritten Text Detection by Synthetic Text 27th

Advancing Handwritten Text Detection by Synthetic Text

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Muth, Markus Peer, Marco Kleber, Florian Sablatnig, Robert Computer Vision Lab Institute of Visual Computing and Human-Centered Technology TU Wien Wien1040 Austria

ISBN: (纸本)9783031784941

In this paper, the usability of synthetic handwritten text to improve machine learning models is examined for the domain of handwritten text detection. We generate synthetic handwritten text by using an existing model based on a style conditioned GAN, and add those texts to scanned documents to mimic handwritten annotations. Object detection models (YOLOv5 and YOLOv8) are trained using synthetic data as a baseline to distinguish handwritten text from remaining content. We study different granularity labels (word-, line- and paragraph-level) and model sizes in our evaluation and show that applying those models to real data results in a mAP@50 of 0.88 and a pixel-level F1@50 of 0.96 for the CVL dataset, and a mAP@50 of 0.72 and F1@50 of 0.89 for SCAN, a custom dataset, created by adding real handwritten annotations to a scientific paper. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Segmentation and Immersive Visualization of Brain Lesions Using Deep Learning and Virtual Reality 19

Segmentation and Immersive Visualization of Brain Lesions Us...

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19th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry, VRCAI 2024

作者： Kelley, Brendan Plabst, Lucas Plabst, Lena Natural User Interaction Lab Colorado State University Fort CollinsCO United States Department of Human-Computer Interaction Julius Maximilian University of Würzburg Würzburg Germany

ISBN: (纸本)9798400713484

Magnetic resonance imaging (MRIs) are commonly used for diagnosing potential neurological disorders, however preparation and interpretation of MRI scans requires professional oversight. Additionally, MRIs are typically viewed as single cross sections of the affected regions which does not always capture the full picture of brain lesions and can be difficult to understand due to 2D's inherent abstraction of our 3D world. To address these challenges we propose a immersive visualization pipeline that combines deep learning image segmentation techniques using a VGG-16 model trained on MRI fluid attenuated inversion recovery (FLAIR) with virtual reality (VR) immersive analytics. Our visualization pipeline begins with our VGG-16 model predicting which regions of the brain are potentially affected by a disease. This output, along with the original scan, are then volumentrically rendered. These renders can then be viewed in VR using an head mounted display (HMD). Within the HMD users can move through the volumentric renderings to view the affected regions and utilize planes to view cross sections of the MRI scans. Our work provides a potential pipeline and tool for diagnosis and care. © 2024 ACM.

关键词： Image segmentation

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I Hear, See, Speak & Do: Bringing Multimodal Information Processing to Intelligent Virtual Agents for Natural human-AI Communication

I Hear, See, Speak & Do: Bringing Multimodal Information Pro...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Ke Li Fariba Mostajeran Sebastian Rings Lucie Kruse Susanne Schmidt Michael Arz Erik Wolf Frank Steinicke Human-Computer Interaction University of Hamburg HIT Lab NZ University of Canterbury

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

In this demo paper, we present an Extended Reality (XR) framework providing a streamlined workflow for creating and interacting with intelligent virtual agents (IVAs) with multimodal information processing capabilities using commercially available artificial intelligence (AI) tools and cloud services such as large language and vision models. The system supports (i) the integration of high-quality, customizable virtual 3D human models for visual representations of IVAs and (ii) multimodal communication with generative AI-driven IVAs in immersive XR, featuring realistic human behavior simulations. Our demo showcases the enormous potential and vast design space of embodied IVAs for various XR applications.

关键词： Solid modeling Visualization Three-dimensional displays Extended reality Metaverse Information processing User interfaces Systems support Haptic interfaces Artificial intelligence

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Toward a Healthier Social Media Experience: Designing 'Inspiration' and 'Reality' Modes to Enhance Digital Well-Being for Generation Z

arXiv

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arXiv 2025年

作者： Kang, Sora Human-Computer Interaction + Design Lab Seoul National University Seoul Korea Republic of

This study presents a dual-mode interface design concept for social media platforms aimed at reducing social comparison in health-related content among Korean MZ (Millennials and Gen-Z) users. The proposed "Inspiration" and "Reality" modes allow users to toggle between curated, idealized posts and more realistic, candid content. This approach aims to alleviate negative psychological effects, such as decreased self-esteem and body dissatisfaction. The pre-study outlines the design framework and discusses potential implications for user satisfaction, perceived authenticity, and mental well-being. © 2025, CC BY-SA.

关键词： Tweets

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Understanding Usability of VR Pointing Methods with a Handheld-style HMD for Onsite Exhibitions 25

Understanding Usability of VR Pointing Methods with a Handhe...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Yuki Abe Kan Kusakabe Myungguen Choi Daisuke Sakamoto Tetsuo Ono Human-Computer Interaction Lab Hokkaido University Sapporo Hokkaido Japan IPLAB University of Tsukuba Tsukuba Ibaraki Japan Human-Computer Interaction Lab Hokkaido University Sapporo Hokkaido Japan and Kyoto Tachibana University Kyoto Japan

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DRDM: A Disentangled Representations Diffusion Model for Synthesizing Realistic Person Images

DRDM: A Disentangled Representations Diffusion Model for Syn...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Enbo Zhang, Yuan Huang, Faliang Zhang, Guangyu Liu, Yang Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer and Information Engineering Nanning Normal University Nanning China College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798350368741

Person image synthesis with controllable body poses and appearances is an essential task owing to the practical needs in the context of virtual try-on, image editing and video production. However, existing methods face significant challenges with details missing, limbs distortion and the garment style deviation. To address these issues, we propose a Disentangled Representations Diffusion Model (DRDM) to generate photo-realistic images from source portraits in specific desired poses and appearances. First, a pose encoder is responsible for encoding pose features into a high-dimensional space to guide the generation of person images. Second, a body-part subspace decoupling block (BSDB) disentangles features from the different body parts of a source figure and feeds them to the various layers of the noise prediction block, thereby supplying the network with rich disentangled features for generating a realistic target image. Moreover, during inference, we develop a parsing map-based disentangled classifier-free guided sampling method, which amplifies the conditional signals of texture and pose. Extensive experimental results on the Deepfashion dataset demonstrate the effectiveness of our approach in achieving pose transfer and appearance control. The associated project can be found at https://***/lovemusiceb/DRDM. © 2025 IEEE.

关键词： Diffusion Model Disentangled Representation human Parsing Person image synthesis

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“I can run at night!": Using Augmented Reality to Support Nighttime Guided Running for Low-vision Runners 25

“I can run at night!": Using Augmented Reality to Support N...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Yuki Abe Keisuke Matsushima Kotaro Hara Daisuke Sakamoto Tetsuo Ono Human-Computer Interaction Lab Hokkaido University Sapporo Hokkaido Japan School of Computing and Information Systems Singapore Management University Singapore Singapore Human-Computer Interaction Lab Hokkaido University Sapporo Hokkaido Japan and Kyoto Tachibana University Kyoto Japan

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AI-Driven Relocation Tracking in Dynamic Kitchen Environments

arXiv

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arXiv 2025年

作者： Esfahani, Arash Nasr Hosseini, Hamed Masouleh, Mehdi Tale Kalhor, Ahmad Sajedi, Hedieh Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Mathematics Statistics and Computer Science University of Tehran Tehran Iran

As smart homes become more prevalent in daily life, the ability to understand dynamic environments is essential which is increasingly dependent on AI systems. This study focuses on developing an intelligent algorithm which can navigate a robot through a kitchen, recognizing objects, and tracking their relocation. The kitchen was chosen as the testing ground due to its dynamic nature as objects are frequently moved, rearranged and replaced. Various techniques, such as SLAM feature-based tracking and deep learning-based object detection (e.g., Faster R-CNN), are commonly used for object tracking. Additionally, methods such as optical flow analysis and 3D reconstruction have also been used to track the relocation of objects. These approaches often face challenges when it comes to problems such as lighting variations and partial occlusions, where parts of the object are hidden in some frames but visible in others. The proposed method in this study leverages the YOLOv5 architecture, initialized with pre-trained weights and subsequently fine-tuned on a custom dataset. A novel method was developed, introducing a frame-scoring algorithm which calculates a score for each object based on its location and features within all frames. This scoring approach helps to identify changes by determining the best-associated frame for each object and comparing the results in each scene, overcoming limitations seen in other methods while maintaining simplicity in design. The experimental results demonstrate an accuracy of 97.72%, a precision of 95.83% and a recall of 96.84% for this algorithm, which highlights the efficacy of the model in detecting spatial changes. © 2025, CC BY.

关键词： Optical flows

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Investigating Composite Relation with a Data-Physicalized Thing through the Deployment of the WavData Lamp 25

Investigating Composite Relation with a Data-Physicalized Th...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Ce Zhong Xiang Li Xizi Wang Junwei Sun Jian Zhao Cheriton School of Computer Science University of Waterloo Waterloo Ontario Canada Huawei Human Machine Interaction Lab Markham Ontario Canada

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