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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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Handwritten Character String Recognition Using Transformer and CNN Features 13

Handwritten Character String Recognition Using Transformer a...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Rakuka, Shunya Morita, Kento Wakabayashi, Tetsushi Mie University Human Computer Interaction Lab Japan

ISBN: (纸本)9798350373332

While character recognition systems applications with restrictions on writing conditions, such as zip code recognition and ledger sheet recognition, are in practical use, the accuracy of free handwritten character string recognition is still low and there are many challenges. Japanese free handwritten character strings are difficult to segment because some Japanese characters consist of multiple connected components and there are no spaces between words. Therefore, in this study, we propose a method for handwritten character string recognition method that does not require character segmentation using Transformer and CNN features. Transformer-based models usually require a large amount of annotated training data. However, our method can be trained with a small amount of annotated training data. Comparative experiments confirm the effectiveness of the proposed method by achieving CER=O.127 while CRNN [1] and TrOCR [2] achieve CER=O.562 and CER=O.174, respectively. The results show that the proposed method is effective for Japanese character string recognition, where the variety of character strings is large and it is difficult to secure a sufficient number of training samples for each character string. © 2024 IEEE.

关键词： Character recognition

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Sound Source Localization in a 3D Multi-Surface Environment Using Learning-Based Methods 12

Sound Source Localization in a 3D Multi-Surface Environment ...

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12th RSI International Conference on Robotics and Mechatronics, ICRoM 2024

作者： Esfahanian, Soroush Rayati, Keyhan Masouleh, Mehdi Tale University of Tehran Human and Robot Interaction Lab Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331529734

This study presents a three-dimensional localization of a ball drop in a multi-surface environment using cost-effective data acquisition devices, and proposes two learning-based methods to improve the baseline classical localization method based on GCC-PHAT-estimated TDOA values. The first proposed method employs a ball drop surface classification executed by Random Forest and XGBoost models to enhance localization performance, whereas the second method conducts region classification by using both basic and multi-branch neural network models, achieving improved and more efficient localization performance. A dataset of real-world sparse ball drop samples was collected in a controlled multi-surface environment for experimental execution, where results demonstrate that the two proposed methods improve the localization performance across various scenarios, with the region classification method significantly outperforming the baseline method, achieving a 17% reduction in the Mean Euclidean Distance error metric. Furthermore, the surface and region classification methods reduce the localization time compared to the baseline method by 50% and 97%, respectively. The obtained results reveal the potential of combining simple learning-based models with affordable microphone sensors for achieving an accurate localization of sparse audio samples in a multi-surface environment. © 2024 IEEE.

关键词： Deep neural networks

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Enhancing STEM curriculum with virtual reality: electricity and magnetism simulations

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Multimedia Tools and Applications 2024年 1-20页

作者： Shinde, Nishant Sarma, Himangshu Human Computer Interaction Lab Computer Science & ampEngineering Indian Institute of Information Technology Sri City 630 Gnan Marg Andhra Pradesh Sri City517646 India

This study explores the use of virtual reality (VR) as an educational tool to enhance the understanding of electricity and magnetism among 10th-grade students in India. A VR application was developed using Unity and Meta Quest 2 (https://***/quest/products/quest-2/;Access Date: 22nd January 2024), featuring thirteen interactive activities-six on electricity and seven on the magnetic effects of electric current. Thirty participants engaged with the VR platform, which offers a hands-on learning experience not provided in the current curriculum. The methodology involved pre- and post-intervention assessments to evaluate the effectiveness of the VR application. Results indicate a significant improvement in students’ comprehension of electricity and magnetism, suggesting that VR can be a cost-effective and engaging supplement to traditional laboratory setups. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Magnetoplasma

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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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AI-Driven Relocation Tracking in Dynamic Kitchen Environments 14

AI-Driven Relocation Tracking in Dynamic Kitchen Environment...

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14th International Conference on computer and Knowledge Engineering, ICCKE 2024

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

ISBN: (纸本)9798331511272

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. ©2024 IEEE.

关键词： Optical flows

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RealityGit: Cross Reality Version Control of R&D Optical Workbench

RealityGit: Cross Reality Version Control of R&D Optical Wor...

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2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2023

作者： Li, Ke Rolff, Tim Bacher, Reinhard Steinicke, Frank Deutsches Elektronen-Synchrotron Desy Germany Universität Hamburg Human-Computer Interaction Department of Informatics Germany Universität Hamburg Computer Vision Department of Informatics Germany

ISBN: (纸本)9798350328912

Multi-user collaboration at research and development (R&D) workbenches in advanced optics laboratories involves updating and documenting the status of hundreds of components. Although there are abundant mixed reality (MR) systems to support the collaboration of spatially co-located users, limited methods are available to facilitate cooperation in the spatial-temporal domain. Inspired by the version control workflow used in software development, we propose RealityGit, a novel cross-reality (CR) system design that leverages the recent advancements of neural radiance fields (NeRF). We illustrate how the NeRF model can be used to channel users with different extended reality (XR) experiences by providing an accurate immersive visual documentation of the historical states of a complex R&D workbench. We demonstrate the feasibility of such a system through an implementation where users could contribute to the version control workflows by integrating historical NeRF scans into their MR devices or providing status reviews by annotating or editing a NeRF model in virtual reality (VR). © 2023 IEEE.

关键词： Mixed reality

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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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Dense 3D Gaussian Splatting Initialization for Sparse Image Data

Dense 3D Gaussian Splatting Initialization for Sparse Image ...

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45th Annual Conference on European Association for computer Graphics, EUROGRAPHICS 2024

作者： Seibt, S. Chang, T. von Rymon Lipinski, B. Latoschik, M.E. Game Tech Lab. Faculty of Computer Science Nuremberg Institute of Technology Nuremberg Germany Human-Computer Interaction Group Institute of Computer Science University of Wuerzburg Wuerzburg Germany

ISBN: (纸本)9783038682394

This paper presents advancements in novel-view synthesis with 3D Gaussian Splatting (3DGS) using a dense and accurate SfM point cloud initialization approach. We address the challenge of achieving photorealistic renderings from sparse image data, where basic 3DGS training may result in suboptimal convergence, thus leading to visual artifacts. The proposed method enhances precision and density of initially reconstructed point clouds by refining 3D positions and extrapolating additional points, even for difficult image regions, e.g. with repeating patterns and suboptimal visual coverage. Our contributions focus on improving "Dense Feature Matching for Structure-from-Motion" (DFM4SfM) based on a homographic decomposition of the image space to support 3DGS training: First, a grid-based feature detection method is introduced for DFM4SfM to ensure a well-distributed 3D Gaussian initialization uniformly over all depth planes. Second, the SfM feature matching is complemented by a geometric plausibility check, priming the homography estimation and thereby improving the initial placement of 3D Gaussians. Experimental results on the NeRF-LLFF dataset demonstrate that this approach achieves superior qualitative and quantitative results, even for fewer views, and the potential for a significantly accelerated 3DGS training with faster convergence. © 2024 The Authors.

关键词： Gaussian distribution

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Leveraging Task-Specific Context to Improve Unsupervised Adaptation for Myoelectric Control

Leveraging Task-Specific Context to Improve Unsupervised Ada...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Eddy, Ethan Campbell, Evan Bateman, Scott Scheme, Erik Institute of Biomedical Engineering University of New Brunswick Human Computer Interaction Lab FrederictonNB Canada

ISBN: (纸本)9798350337020

While there has been renewed interest in the use of myoelectric control for general-purpose applications, the burden of training and maintaining robust models still limits its real-world viability. Online unsupervised adaptation has been proposed to solve this issue by updating the model using predicted pseudo-labels in real time during regular device use. Until now, however, these unsupervised strategies have been limited as they rely on the very classifier outputs they are adapting, making them ill-suited when there is a drastic shift in the input space (e.g., after donning and doffing a device) or there is insufficient training data. In such situations, leveraging context (i.e., task-specific information that can help understand or assess a circumstance) could provide additional guidance for adaptation and improve its robustness. Although difficult to extract in traditional prosthesis control use cases without additional sensors, context may be more readily available in other general-purpose applications, such as in human-computer interaction. In this study, we explore leveraging context, both positive (i.e., reinforcing correct actions) and negative (i.e., correcting poor actions), for conditioning pseudo-label predictions within an adaptive gamified target acquisition setting. The results show that leveraging this additional con-text significantly outperforms the current state-of-the-art high-confidence unsupervised adaptation (p © 2023 IEEE.

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