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作者： Mazumdar, Angshuman Mousas, Christos Department of Computer Graphics Technology Purdue University West LafayetteIN United States

This article presents a methodology for automatically synthesizing a virtual population (pedestrians placed in a virtual environment) that impacts a user with a specified affective experience. The pipeline began by developing a dataset of behaviors that could be assigned to virtual characters. Next, an annotation phase assigned affective responses of participants to each character's behavior. The design considerations of our affective multicharacter virtual reality experience were then encoded to cost terms and assigned to a total cost function. This method allowed the developer to control the priority and the targets of the cost terms, and given the user inputs, our application could optimize the multicharacter experience using a Markov chain Monte Carlo method known as simulated annealing. A user study was conducted to investigate whether our method could synthesize virtual reality multicharacter experiences that affect participants in an expected way. The results of our study showed that the three different synthesized multicharacter experiences (low, medium, and high negative affect) were perceived as expected by participants;therefore, we argue that we can indeed automatically synthesize virtual reality multicharacter experiences that impact participants' affect levels in an expected way. Limitations and future research directions are discussed. © 2021 John Wiley & Sons, Ltd.

关键词： Virtual reality

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Robots Saving Lives: A Literature Review About Search and Rescue (SAR) in Harsh Environments

Robots Saving Lives: A Literature Review About Search and Re...

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IEEE Symposium on Intelligent Vehicle

作者： Kailin Tong Yuxi Hu Berin Dikic Selim Solmaz Friedrich Fraundorfer Daniel Watzenig Virtual Vehicle Research GmbH Graz Austria Institute of Computer Graphics and Vision at Graz University of Technology Graz Austria

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

In recent years, the rise in both natural and man-made disasters, along with armed conflicts and terrorist threats, has elevated the demand for Search and Rescue (SAR) missions worldwide. This paper underscores the critical necessity to enhance SAR capacity, safety, and capabilities, with a primary goal of reducing response times through the integration of robots into SAR operations. The examination of research on robotized SAR highlights deficiencies in both software and hardware, particularly focusing on perception systems for robotized SAR platforms.

关键词： Intelligent vehicles Terrorism Disasters Bibliographies Focusing Software Hardware

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A Systematic Review of Generative AI on Game Character Creation: Applications, Challenges, and Future Trends

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IEEE Transactions on Games 2025年

作者： Wu, Zhenhua Chen, Zhuohao Zhu, Di Mousas, Christos Kao, Dominic Purdue University Department of Computer Information Technology West LafayetteIN United States Purdue University Department of Computer Graphics Technology West LafayetteIN United States

In this paper, we review the impact of generative artificial intelligence (AI) on game character creation. We critically examine the application of AI-driven computer graphics (AICG) technology across the character creation workflow, including concept generation, clothing design, modeling, props, cultural embedding, personality traits, and behaviors. Our research identifies potential applications, challenges, and future trends of these technologies in game development. Furthermore, it explores how AI and large language models (LLMs) can streamline workflows, automate asset generation, and reduce technical barriers in character creation. In this systematic review, we provide valuable insights for AI developers, game designers, and researchers. © 2018 IEEE.

关键词： Game design

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Gamified Virtual Reality Training for Visuospatial Ability in Neuroradiology

Gamified Virtual Reality Training for Visuospatial Ability i...

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作者： Allgaier, Mareen Jered Härtel, Tim Zubel, Seraphine Thormann, Maximilian Behme, Daniel Preim, Bernhard Saalfeld, Sylvia Department of Simulation and Graphics Otto-von-Guericke-University Magdeburg Universitätsplatz 2 Magdeburg39106 Germany Department of Neuroradiology University Hospital Magdeburg Magdeburg39120 Germany Department of Computer Science and Automation Ilmenau University of Technology Ilmenau98693 Germany

During neuroradiological interventions such as treating intracranial aneurysms, neuroradiologists have to navigate a catheter in the 3D anatomy while watching a 2D projection of the vessels. To train this visuospatial ability, we present a virtual reality application where the user has to identify anatomically relevant positions on the Circle of Willis based on a 2D projection image. This image is generated based on a 3D model using a Fresnel shader. To increase motivation, which is an important aspect of training, we included several game elements. We qualitatively evaluated our application with two neuroradiologists. Although they rated our implementation of the task as difficult due to the small image section, both participants emphasized the importance of this training. Even small changes that have been suggested can adjust the difficulty, leading to an appropriate training that could be integrated into the curriculum. The results also indicate that the included game elements are motivating and that the application in general has a high usability. The proposed immersive virtual reality application provides training for visuospatial ability in a concrete medical context. Due to gamification, fun, and motivation are increased, which are two important factors of training affecting the frequency of use and outcome. © 2024 by Walter de Gruyter Berlin/Boston.

关键词： Virtual environments

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MIPGAN-Generating Strong and High Quality Morphing Attacks Using Identity Prior Driven GAN

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity Science 2021年第3期3卷 365-383页

作者： Zhang, Haoyu Venkatesh, Sushma Ramachandra, Raghavendra Raja, Kiran Damer, Naser Busch, Christoph Norwegian University of Science and Technology Gjovik2815 Norway Department of Igd Fraunhofer Institute for Computer Graphics Research Igd Darmstadt64283 Germany

Face morphing attacks target to circumvent Face Recognition Systems (FRS) by employing face images derived from multiple data subjects (e.g., accomplices and malicious actors). Morphed images can be verified against contributing data subjects with a reasonable success rate, given they have a high degree of facial resemblance. The success of morphing attacks is directly dependent on the quality of the generated morph images. We present a new approach for generating strong attacks extending our earlier framework for generating face morphs. We present a new approach using an Identity Prior Driven Generative Adversarial Network, which we refer to as MIPGAN (Morphing through Identity Prior driven GAN). The proposed MIPGAN is derived from the StyleGAN with a newly formulated loss function exploiting perceptual quality and identity factor to generate a high quality morphed facial image with minimal artefacts and with high resolution. We demonstrate the proposed approach's applicability to generate strong morphing attacks by evaluating its vulnerability against both commercial and deep learning based Face Recognition System (FRS) and demonstrate the success rate of attacks. Extensive experiments are carried out to assess the FRS's vulnerability against the proposed morphed face generation technique on three types of data such as digital images, re-digitized (printed and scanned) images, and compressed images after re-digitization from newly generated MIPGAN Face Morph Dataset. The obtained results demonstrate that the proposed approach of morph generation poses a high threat to FRS. © 2019 IEEE.

关键词： Face recognition

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A Multi-Armed Bandits Learning-Based Approach to Service Caching in Edge Computing Environment 30th

A Multi-Armed Bandits Learning-Based Approach to Service Ca...

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30th International Conference on Web Services, ICWS 2023

作者： Li, Jinpeng Zhao, Jiale Chen, Peng Xia, Yunni Li, Fan Li, Yin Zeng, Feng Liu, Hui School of Computer Chongqing University Chongqing400030 China School of Computer and Software Engineering Xihua University Chengdu610039 China Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu610065 China Guangzhou Institute of Software Application Technology Guangzhou510990 China Limited Shenzhen518129 China School of Computer Science and Technology Beijing Institute of Technology Beijing100083 China

ISBN: (纸本)9783031448355

Mobile edge computing (MEC) is a newly emerging concept that provides significant local computing power and reduces end-to-end latency. In MEC environments, caching frequently accessed services on edge servers effectively reduces latency and improves system responsiveness. An ongoing research topic in such a cachable MEC context is to design novel algorithms for yielding high-quality caching decision that guarantee high user-perceived quality-of-service (QoS) and high system responsiveness of delivery of cached content with the difference of caching capacities of edge servers and diversified content popularity appropriately addressed. In this article, we propose a multi-armed bandits learning-based method busing a Thompson sampling for generating caching decisions. We introduce a genetic multi-armed bandits algorithm (GMAB), which synthesizes the genetic algorithm (GA) and multi-armed bandits (MAB), for optimizing caching effectiveness with timing and space constraints. The experiment results show that GMAB outperforms traditional methods in terms of multiple aspects. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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Deep Learning-Based Point Cloud Registration for Augmented Reality-Guided Surgery

Deep Learning-Based Point Cloud Registration for Augmented R...

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IEEE International Symposium on Biomedical Imaging

作者： Maximilian Weber Daniel Wild Jens Kleesiek Jan Egger Christina Gsaxner Institute of Computer Graphics and Vision Graz University of Technology Institute for Artificial Intelligence in Medicine University Hospital Essen (AöR)

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Point cloud registration aligns 3D point clouds using spatial transformations. It is an important task in computer vision, with applications in areas such as augmented reality (AR) and medical imaging. This work explores the intersection of two research trends: the integration of AR into image-guided surgery and the use of deep learning for point cloud registration. The main objective is to evaluate the feasibility of applying deep learning-based point cloud registration methods for image-to-patient registration in augmented reality-guided surgery. We created a dataset of point clouds from medical imaging and corresponding point clouds captured with a popular AR device, the HoloLens 2. We evaluate three well-established deep learning models in registering these data pairs. While we find that some deep learning methods show promise, we show that a conventional registration pipeline still outperforms them on our challenging dataset.

关键词： Point cloud compression Deep learning Accuracy Three-dimensional displays Noise Surgery Registers

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Secured Digital and QR Code Payments: A Comparative Analysis of Payment Models

Secured Digital and QR Code Payments: A Comparative Analysis...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Bhavna Galhotra Shalini Bhaskar Bajaj Aman Jatain Vivek Jaglan Computer science dept Amity University JIMS Computer science dept Amity University Department of Computer Science & Technology Manav Rachna University Research Graphics Era University Dehradun

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

The internet's explosive expansion has paved the way for a wide range of smartphone applications that use QR codes and digital wallets to facilitate online payments. Security is a major concern for web-based applications since they are being delivered over the Internet at a rapid pace. Due to unlawful access to many accounts, which causes problems with end-to-end security, the security of these applications has grown to be a top concern for both customers and business owners. This work has addressed the several models that are accessible for security applications, also provided a comparative analysis of all the models, taking into account their security privacy, adaptability, accuracy reliability, vulnerabilities and suitability for digital wallets and online payment systems through QR codes.

关键词： Adaptation models Analytical models Privacy Biological system modeling QR codes Logic gates Tokenization Mobile applications Reliability Standards

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Robust Teeth Detection in 3D Dental Scans by Automated Multi-view Landmarking

Robust Teeth Detection in 3D Dental Scans by Automated Multi...

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15th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2022

作者： Kubík, Tibor Španĕl, Michal Department of Computer Graphics and Multimedia Faculty of Information Technology Brno University of Technology Brno Czech Republic TESCAN 3DIM Brno Czech Republic

Landmark detection is frequently an intermediate step in medical data analysis. More and more often, these data are represented in the form of 3D models. An example is a 3D intraoral scan of dentition used in or thodontics, where landmarking is notably challenging due to malocclusion, teeth shift, and frequent teeth missing. What’s more, in terms of 3D data, the DNN processing comes with high memory and computational time requirements, which do not meet the needs of clinical applications. We present a robust method for tooth landmark detection based on a multi-view approach, which transforms the task into a 2D domain, where the suggested network detects landmarks by heatmap regression from several viewpoints. Additionally, we propose a post-processing based on Multi-view Confidence and Maximum Heatmap Activation Confidence, which can robustly determine whether a tooth is missing or not. Experiments have shown that the combination of Attention U-Net, 100 viewpoints, and RANSAC consensus method is able to detect landmarks with an error of 075 096mm. In addition to the promising accuracy, our method is robust to missing teeth, as it can correctly detect the presence of teeth in 97.68% cases. © 2022 by SCITEPRESS–Science and technology Publications, Lda. All rights reserved.

关键词： Deep neural networks

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Diffusion-based generation of Histopathological Whole Slide Images at a Gigapixel scale

Diffusion-based generation of Histopathological Whole Slide ...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Robert Harb Thomas Pock Heimo Müller Institute of Computer Graphics and Vision Graz University of Technology Austria Diagnostic and Research Institute of Pathology Medical University of Graz Austria

We present a novel diffusion-based approach to generate synthetic histopathological Whole Slide Images (WSIs) at an unprecedented gigapixel scale. Synthetic WSIs have many potential applications: They can augment training datasets to enhance the performance of many computational pathology applications. They allow the creation of synthesized copies of datasets that can be shared without violating privacy regulations. Or they can facilitate learning representations of WSIs without requiring data annotations. Despite this variety of applications, no existing deep-learning-based method generates WSIs at their typically high resolutions. Mainly due to the high computational complexity. Therefore, we propose a novel coarse-to-fine sampling scheme to tackle image generation of high-resolution WSIs. In this scheme, we increase the resolution of an initial low-resolution image to a high-resolution WSI. Particularly, a diffusion model sequentially adds fine details to images and increases their resolution. In our experiments, we train our method with WSIs from the TCGA-BRCA dataset. Additionally to quantitative evaluations, we also performed a user study with pathologists. The study results suggest that our generated WSIs resemble the structure of real WSIs.

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