Computer Animation and Virtual Worlds最新文献_第8页

Virtual Roaming of Cultural Heritage Based on Image Processing 基于图像处理的文化遗产虚拟漫游

IF 0.9 4区计算机科学

Computer Animation and Virtual Worlds Pub Date : 2024-11-10 DOI: 10.1002/cav.70000

Junzhe Chen, Xing She, Yuanxin Fan, Wenwen Shao

引用次数: 0

PainterAR: A Self-Painting AR Interface for Mobile Devices PainterAR：移动设备的自绘 AR 界面

IF 0.9 4区计算机科学

Computer Animation and Virtual Worlds Pub Date : 2024-11-07 DOI: 10.1002/cav.2296

Yuan Ma, Yinghan Shi, Lizhi Zhao, Xuequan Lu, Been-Lirn Duh, Meili Wang

{"title":"PainterAR: A Self-Painting AR Interface for Mobile Devices","authors":"Yuan Ma, Yinghan Shi, Lizhi Zhao, Xuequan Lu, Been-Lirn Duh, Meili Wang","doi":"10.1002/cav.2296","DOIUrl":"https://doi.org/10.1002/cav.2296","url":null,"abstract":"<div>\u0000 \u0000 <p>Painting is a complex and creative process that involves the use of various drawing skills to create artworks. The concept of training artificial intelligence models to imitate this process is referred to as neural painting. To enable ordinary people to engage in the process of painting, we propose PainterAR, a novel interface that renders any paintings stroke-by-stroke in an immersive and realistic augmented reality (AR) environment. PainterAR is composed of two components: the neural painting model and the AR interface. Regarding the neural painting model, unlike previous models, we introduce the Kullback–Leibler divergence to replace the original Wasserstein distance existed in the baseline paint transformer model, which solves an important problem of encountering different scales of strokes (big or small) during painting. We then design an interactive AR interface, which allows users to upload an image and display the creation process of the neural painting model on the virtual drawing board. Experiments demonstrate that the paintings generated by our improved neural painting model are more realistic and vivid than previous neural painting models. The user study demonstrates that users prefer to control the painting process interactively in our AR environment.</p>\u0000 </div>","PeriodicalId":50645,"journal":{"name":"Computer Animation and Virtual Worlds","volume":"35 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142641515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Decoupled Edge Physics Algorithms for Collaborative XR Simulations 用于 XR 协作模拟的解耦边缘物理算法

IF 0.9 4区计算机科学

Computer Animation and Virtual Worlds Pub Date : 2024-11-03 DOI: 10.1002/cav.2294

George Kokiadis, Antonis Protopsaltis, Michalis Morfiadakis, Nick Lydatakis, George Papagiannakis

{"title":"Decoupled Edge Physics Algorithms for Collaborative XR Simulations","authors":"George Kokiadis, Antonis Protopsaltis, Michalis Morfiadakis, Nick Lydatakis, George Papagiannakis","doi":"10.1002/cav.2294","DOIUrl":"https://doi.org/10.1002/cav.2294","url":null,"abstract":"<div>\u0000 \u0000 <p>This work proposes a novel approach to transform any modern game engine pipeline, for optimized performance and enhanced user experiences in extended reality (XR) environments decoupling the physics engine from the game engine pipeline and using a client-server <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>N</mi>\u0000 <mo>−</mo>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 <annotation>$$ N-1 $$</annotation>\u0000 </semantics></math> architecture creates a scalable solution, efficiently serving multiple graphics clients on head-mounted displays (HMDs) with a single physics engine on edge-cloud infrastructure. This approach ensures better synchronization in multiplayer scenarios without introducing overhead in single-player experiences, maintaining session continuity despite changes in user participation. Relocating the Physics Engine to an edge or cloud node reduces strain on local hardware, dedicating more resources to high-quality rendering and unlocking the full potential of untethered HMDs. We present four algorithms that decouple the physics engine, increasing frame rates and Quality of Experience (QoE) in VR simulations, supporting advanced interactions, numerous physics objects, and multiuser sessions with over 100 concurrent users. Incorporating a Geometric Algebra interpolator reduces inter-calls between dissected parts, maintaining QoE and easing network stress. Experimental validation, with more than 100 concurrent users, 10,000 physics objects, and softbody simulations, confirms the technical viability of the proposed architecture, showcasing transformative capabilities for more immersive and collaborative XR applications without compromising performance.</p>\u0000 </div>","PeriodicalId":50645,"journal":{"name":"Computer Animation and Virtual Worlds","volume":"35 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VTSIM: Attention-Based Recurrent Neural Network for Intersection Vehicle Trajectory Simulation VTSIM：用于交叉路口车辆轨迹模拟的基于注意力的递归神经网络

IF 0.9 4区计算机科学

Computer Animation and Virtual Worlds Pub Date : 2024-11-03 DOI: 10.1002/cav.2298

Jingyao Liu, Tianlu Mao, Zhaoqi Wang

{"title":"VTSIM: Attention-Based Recurrent Neural Network for Intersection Vehicle Trajectory Simulation","authors":"Jingyao Liu, Tianlu Mao, Zhaoqi Wang","doi":"10.1002/cav.2298","DOIUrl":"https://doi.org/10.1002/cav.2298","url":null,"abstract":"<div>\u0000 \u0000 <p>Simulating vehicle trajectories at intersections is one of the challenging tasks in traffic simulation. Existing methods are often ineffective due to the complexity and diversity of lane topologies at intersections, as well as the numerous interactions affecting vehicle motion. To address this issue, we propose a deep learning based vehicle trajectory simulation method. First, we employ a vectorized representation to uniformly extract features from traffic elements such as pedestrians, vehicles, and lanes. By fusing all factors that influence vehicle motion, this representation makes our method suitable for a variety of intersections. Second, we propose a deep learning model, which has an attention network to dynamically extract features from the surrounding environment of the vehicles. To address the issue of vehicles continuously entering and exiting the simulation scene, we employ an asynchronous recurrent neural network for the extraction of temporal features. Comparative evaluations against existing rule-based and deep learning-based methods demonstrate our model's superior simulation accuracy. Furthermore, experimental validation on public datasets demonstrates that our model can simulate vehicle trajectories among the urban intersections with different topologies including those not present in the training dataset.</p>\u0000 </div>","PeriodicalId":50645,"journal":{"name":"Computer Animation and Virtual Worlds","volume":"35 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Training Climbing Roses by Constrained Graph Search 通过受限图谱搜索训练攀爬玫瑰

IF 0.9 4区计算机科学

Computer Animation and Virtual Worlds Pub Date : 2024-10-15 DOI: 10.1002/cav.2297

Wataru Umezawa, Tomohiko Mukai

引用次数: 0

Acanthus ornament generation using layout subdivisions with parameterized motifs 利用带参数化图案的布局细分生成刺桐装饰物

IF 0.9 4区计算机科学

Computer Animation and Virtual Worlds Pub Date : 2024-09-24 DOI: 10.1002/cav.2292

Yuka Komeda, Wataru Umezawa, Tomohiko Mukai

引用次数: 0

Human action recognition in immersive virtual reality based on multi-scale spatio-temporal attention network 基于多尺度时空注意力网络的沉浸式虚拟现实中的人类动作识别

IF 0.9 4区计算机科学

Computer Animation and Virtual Worlds Pub Date : 2024-09-23 DOI: 10.1002/cav.2293

Zhiyong Xiao, Yukun Chen, Xinlei Zhou, Mingwei He, Li Liu, Feng Yu, Minghua Jiang

{"title":"Human action recognition in immersive virtual reality based on multi-scale spatio-temporal attention network","authors":"Zhiyong Xiao, Yukun Chen, Xinlei Zhou, Mingwei He, Li Liu, Feng Yu, Minghua Jiang","doi":"10.1002/cav.2293","DOIUrl":"https://doi.org/10.1002/cav.2293","url":null,"abstract":"<p>Wearable human action recognition (HAR) has practical applications in daily life. However, traditional HAR methods solely focus on identifying user movements, lacking interactivity and user engagement. This paper proposes a novel immersive HAR method called MovPosVR. Virtual reality (VR) technology is employed to create realistic scenes and enhance the user experience. To improve the accuracy of user action recognition in immersive HAR, a multi-scale spatio-temporal attention network (MSSTANet) is proposed. The network combines the convolutional residual squeeze and excitation (CRSE) module with the multi-branch convolution and long short-term memory (MCLSTM) module to extract spatio-temporal features and automatically select relevant features from action signals. Additionally, a multi-head attention with shared linear mechanism (MHASLM) module is designed to facilitate information interaction, further enhancing feature extraction and improving accuracy. The MSSTANet network achieves superior performance, with accuracy rates of 99.33% and 98.83% on the publicly available WISDM and PAMPA2 datasets, respectively, surpassing state-of-the-art networks. Our method showcases the potential to display user actions and position information in a virtual world, enriching user experiences and interactions across diverse application scenarios.</p>","PeriodicalId":50645,"journal":{"name":"Computer Animation and Virtual Worlds","volume":"35 5","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Knitted fabric simulation: A survey 针织物模拟：一项调查

IF 0.9 4区计算机科学

Computer Animation and Virtual Worlds Pub Date : 2024-08-20 DOI: 10.1002/cav.2262

Xinrong Hu, Meng Wang, Junping Liu, Jinxing Liang, Kai Yang, Ruiqi Luo, Fei Fang, Tao Peng

引用次数: 0

Hybrid attention adaptive sampling network for human pose estimation in videos 用于视频中人体姿态估计的混合注意力自适应采样网络

IF 0.9 4区计算机科学

Computer Animation and Virtual Worlds Pub Date : 2024-08-20 DOI: 10.1002/cav.2244

Qianyun Song, Hao Zhang, Yanan Liu, Shouzheng Sun, Dan Xu

引用次数: 0

Nadine: A large language model-driven intelligent social robot with affective capabilities and human-like memory 纳丁大型语言模型驱动的智能社交机器人，具有情感能力和类人记忆力

IF 0.9 4区计算机科学

Computer Animation and Virtual Worlds Pub Date : 2024-08-15 DOI: 10.1002/cav.2290

Hangyeol Kang, Maher Ben Moussa, Nadia Magnenat Thalmann

{"title":"Nadine: A large language model-driven intelligent social robot with affective capabilities and human-like memory","authors":"Hangyeol Kang, Maher Ben Moussa, Nadia Magnenat Thalmann","doi":"10.1002/cav.2290","DOIUrl":"https://doi.org/10.1002/cav.2290","url":null,"abstract":"<p>In this work, we describe our approach to developing an intelligent and robust social robotic system for the Nadine social robot platform. We achieve this by integrating large language models (LLMs) and skillfully leveraging the powerful reasoning and instruction-following capabilities of these types of models to achieve advanced human-like affective and cognitive capabilities. This approach is novel compared to the current state-of-the-art LLM-based agents which do not implement human-like long-term memory or sophisticated emotional capabilities. We built a social robot system that enables generating appropriate behaviors through multimodal input processing, bringing episodic memories accordingly to the recognized user, and simulating the emotional states of the robot induced by the interaction with the human partner. In particular, we introduce an LLM-agent frame for social robots, social robotics reasoning and acting, serving as a core component for the interaction module in our system. This design has brought forth the advancement of social robots and aims to increase the quality of human–robot interaction.</p>","PeriodicalId":50645,"journal":{"name":"Computer Animation and Virtual Worlds","volume":"35 4","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cav.2290","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141986061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0