Computer Animation and Virtual Worlds最新文献_第2页

A Multi-Model Approach for Attention Prediction in Gaming Environments for Autistic Children

IF 0.9 4区计算机科学

Computer Animation and Virtual Worlds Pub Date : 2025-01-28 DOI: 10.1002/cav.70010

P. Valarmathi, A. Packialatha

{"title":"A Multi-Model Approach for Attention Prediction in Gaming Environments for Autistic Children","authors":"P. Valarmathi, A. Packialatha","doi":"10.1002/cav.70010","DOIUrl":"https://doi.org/10.1002/cav.70010","url":null,"abstract":"<div>\u0000 \u0000 <p>Autism spectrum disorder (ASD) is a neurological condition that affects an individual's mental development. This research work implements a multimodality input-based virtual reality (VR)-enabled attention prediction approach in gaming for children with autism. Initially, the multimodal inputs such as face image, electroencephalogram (EEG) signal, and data are individually processed by both the preprocessing and feature extraction procedures. Subsequently, a hybrid classification model with classifiers such as improved deep convolutional neural network (IDCNN) and long short term memory (LSTM) is utilized in expression detection by concatenating the resultant features obtained from the feature extraction procedure. Here, the conventional deep convolutional neural network (DCNN) approach is improved by a novel block-knowledge-based processing with a proposed sine-hinge loss function. Finally, an improved weighted mutual information process is employed in attention prediction. Moreover, this proposed attention prediction model is analyzed by simulation and experimental analyses. The effectiveness of the proposed model is significantly proved by the experimental results obtained from various analyses.</p>\u0000 </div>","PeriodicalId":50645,"journal":{"name":"Computer Animation and Virtual Worlds","volume":"36 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120500","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

WDANet: Exploring Stylized Animation via Diffusion Model for Woodcut-Style Design

IF 0.9 4区计算机科学

Computer Animation and Virtual Worlds Pub Date : 2025-01-08 DOI: 10.1002/cav.70007

Yangchunxue Ou, Jingjun Xu

{"title":"WDANet: Exploring Stylized Animation via Diffusion Model for Woodcut-Style Design","authors":"Yangchunxue Ou, Jingjun Xu","doi":"10.1002/cav.70007","DOIUrl":"https://doi.org/10.1002/cav.70007","url":null,"abstract":"<div>\u0000 \u0000 <p>Stylized animation strives for innovation and bold visual creativity. Integrating the inherent strong visual impact and color contrast of woodcut style into such animations is both appealing and challenging, especially during the design phase. Traditional woodcut methods, hand-drawing, and previous computer-aided techniques face challenges such as dwindling design inspiration, lengthy production times, and complex adjustment procedures. To address these issues, we propose a novel network framework, the Woodcut-style Design Assistant Network (WDANet). Our research is the first to use diffusion models to streamline the woodcut-style design process. We curate the Woodcut-62 dataset, which features works from 62 renowned historical artists, to train WDANet in capturing and learning the aesthetic nuances of woodcut prints. WDANet, based on the denoising U-Net, effectively decouples content and style features. It allows users to input or slightly modify a text description to quickly generate accurate, high-quality woodcut-style designs, saving time and offering flexibility. Quantitative and qualitative analyses, along with user studies, confirm that WDANet outperforms current state-of-the-art methods in generating woodcut-style images, demonstrating its value as a design aid.</p>\u0000 </div>","PeriodicalId":50645,"journal":{"name":"Computer Animation and Virtual Worlds","volume":"36 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113147","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

Novel View Synthesis Based on Similar Perspective

IF 0.9 4区计算机科学

Computer Animation and Virtual Worlds Pub Date : 2025-01-07 DOI: 10.1002/cav.70006

Wenkang Huang

{"title":"Novel View Synthesis Based on Similar Perspective","authors":"Wenkang Huang","doi":"10.1002/cav.70006","DOIUrl":"https://doi.org/10.1002/cav.70006","url":null,"abstract":"<div>\u0000 \u0000 <p>Neural radiance fields (NeRF) technology has garnered significant attention due to its exceptional performance in generating high-quality novel view images. In this study, we propose an innovative method that leverages the similarity between views to enhance the quality of novel view image generation. Initially, a pre-trained NeRF model generates an initial novel view image, which is subsequently compared and subjected to feature transfer with the most similar reference view from the training dataset. Following this, the reference view that is most similar to the initial novel view is selected from the training dataset. We designed a texture transfer module that employs a strategy progressing from coarse-to-fine, effectively integrating salient features from the reference view into the initial image, thus producing more realistic novel view images. By using similar views, this approach not only improves the quality of novel perspective images but also incorporates the training dataset as a dynamic information pool into the novel view integration process. This allows for the continuous acquisition and utilization of useful information from the training data throughout the synthesis process. Extensive experimental validation shows that using similar views to provide scene information significantly outperforms existing neural rendering techniques in enhancing the realism and accuracy of novel view images.</p>\u0000 </div>","PeriodicalId":50645,"journal":{"name":"Computer Animation and Virtual Worlds","volume":"36 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143112801","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

Body Part Segmentation of Anime Characters 动漫人物的身体部位分割

IF 0.9 4区计算机科学

Computer Animation and Virtual Worlds Pub Date : 2024-12-17 DOI: 10.1002/cav.2295

Zhenhua Ou, Xueting Liu, Chengze Li, Zhenkun Wen, Ping Li, Zhijian Gao, Huisi Wu

{"title":"Body Part Segmentation of Anime Characters","authors":"Zhenhua Ou, Xueting Liu, Chengze Li, Zhenkun Wen, Ping Li, Zhijian Gao, Huisi Wu","doi":"10.1002/cav.2295","DOIUrl":"https://doi.org/10.1002/cav.2295","url":null,"abstract":"<div>\u0000 \u0000 <p>Semantic segmentation is an important approach to present the perceptual semantic understanding of an image, which is of significant usage in various applications. Especially, body part segmentation is designed for segmenting body parts of human characters to assist different editing tasks, such as style editing, pose transfer, and animation production. Since segmentation requires pixel-level precision in semantic labeling, classic heuristics-based methods generally have unstable performance. With the deployment of deep learning, a great step has been taken in segmenting body parts of human characters in natural photographs. However, the existing models are purely trained on natural photographs and generally obtain incorrect segmentation results when applied on anime character images, due to the large visual gap between training data and testing data. In this article, we present a novel approach to achieving body part segmentation of cartoon characters via a pose-based graph-cut formulation. We demonstrate the use of the acquired body part segmentation map in various image editing tasks, including conditional generation, style manipulation, pose transfer, and video-to-anime.</p>\u0000 </div>","PeriodicalId":50645,"journal":{"name":"Computer Animation and Virtual Worlds","volume":"35 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861507","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

Fast and Incremental 3D Model Renewal for Urban Scenes With Appearance Changes 具有外观变化的城市场景的快速增量3D模型更新

IF 0.9 4区计算机科学

Computer Animation and Virtual Worlds Pub Date : 2024-12-11 DOI: 10.1002/cav.70004

Yuan Xiong, Zhong Zhou

引用次数: 0

Diverse Motions and Responses in Crowd Simulation 人群模拟中的各种运动和反应

IF 0.9 4区计算机科学

Computer Animation and Virtual Worlds Pub Date : 2024-11-26 DOI: 10.1002/cav.70002

Yiwen Ma, Tingting Liu, Zhen Liu

{"title":"Diverse Motions and Responses in Crowd Simulation","authors":"Yiwen Ma, Tingting Liu, Zhen Liu","doi":"10.1002/cav.70002","DOIUrl":"https://doi.org/10.1002/cav.70002","url":null,"abstract":"<div>\u0000 \u0000 <p>A challenge in crowd simulation is to generate diverse pedestrian motions in virtual environments. Nowadays, there is a greater emphasis on the diversity and authenticity of pedestrian movements in crowd simulation, while most traditional models primarily focus on collision avoidance and motion continuity. Recent studies have enhanced realism through data-driven approaches that exploit the movement patterns of pedestrians from real data for trajectory prediction. However, they have not taken into account the body-part motions of pedestrians. Differing from these approaches, we innovatively utilize learning-based character motion and physics animation to enhance the diversity of pedestrian motions in crowd simulation. The proposed method can provide a promising avenue for more diverse crowds and is realized by a novel framework that deeply integrates motion synthesis and physics animation with crowd simulation. The framework consists of three main components: the learning-based motion generator, which is responsible for generating diverse character motions; the hybrid simulation, which ensures the physical realism of pedestrian motions; and the velocity-based interface, which assists in integrating navigation algorithms with the motion generator. Experiments have been conducted to verify the effectiveness of the proposed method in different aspects. The visual results demonstrate the feasibility of our approach.</p>\u0000 </div>","PeriodicalId":50645,"journal":{"name":"Computer Animation and Virtual Worlds","volume":"35 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737568","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

A Facial Motion Retargeting Pipeline for Appearance Agnostic 3D Characters 面向外观无关 3D 角色的面部运动重定位管道

IF 0.9 4区计算机科学

Computer Animation and Virtual Worlds Pub Date : 2024-11-19 DOI: 10.1002/cav.70001

ChangAn Zhu, Chris Joslin

{"title":"A Facial Motion Retargeting Pipeline for Appearance Agnostic 3D Characters","authors":"ChangAn Zhu, Chris Joslin","doi":"10.1002/cav.70001","DOIUrl":"https://doi.org/10.1002/cav.70001","url":null,"abstract":"<p>3D facial motion retargeting has the advantage of capturing and recreating the nuances of human facial motions and speeding up the time-consuming 3D facial animation process. However, the facial motion retargeting pipeline is limited in reflecting the facial motion's semantic information (i.e., meaning and intensity), especially when applied to nonhuman characters. The retargeting quality heavily relies on the target face rig, which requires time-consuming preparation such as 3D scanning of human faces and modeling of blendshapes. In this paper, we propose a facial motion retargeting pipeline aiming to provide fast and semantically accurate retargeting results for diverse characters. The new framework comprises a target face parameterization module based on face anatomy and a compatible source motion interpretation module. From the quantitative and qualitative evaluations, we found that the proposed retargeting pipeline can naturally recreate the expressions performed by a motion capture subject in equivalent meanings and intensities, such semantic accuracy extends to the faces of nonhuman characters without labor-demanding preparations.</p>","PeriodicalId":50645,"journal":{"name":"Computer Animation and Virtual Worlds","volume":"35 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cav.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142674057","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

Enhancing Front-End Security: Protecting User Data and Privacy in Web Applications 加强前端安全：在网络应用程序中保护用户数据和隐私

IF 0.9 4区计算机科学

Computer Animation and Virtual Worlds Pub Date : 2024-11-13 DOI: 10.1002/cav.70003

Oleksandr Tkachenko, Vadim Goncharov, Przemysław Jatkiewicz

{"title":"Enhancing Front-End Security: Protecting User Data and Privacy in Web Applications","authors":"Oleksandr Tkachenko, Vadim Goncharov, Przemysław Jatkiewicz","doi":"10.1002/cav.70003","DOIUrl":"https://doi.org/10.1002/cav.70003","url":null,"abstract":"<div>\u0000 \u0000 <p>Conducting research on this subject remains relevant in light of the rapid development of technology and the emergence of new threats in cybersecurity, requiring constant updating of knowledge and protection methods. The purpose of the study is to identify effective front-end security methods and technologies that help ensure the protection of user data and their privacy when using web applications or sites. A methodology that defines the steps and processes for effective front-end security and user data protection is developed. The research identifies the primary security threats, including cross-site scripting (XSS), cross-site request forgery (CSRF), and SQL injections, and evaluates existing front-end security methods such as Content Security Policy (CSP), HTTPS, authentication, and authorization mechanisms. The findings highlight the effectiveness of these measures in mitigating security risks, providing a clear assessment of their advantages and limitations. Key recommendations for developers include the integration of modern security protocols, regular updates, and comprehensive security training. This study offers practical insights to improve front-end security and enhance user data protection in an evolving digital landscape.</p>\u0000 </div>","PeriodicalId":50645,"journal":{"name":"Computer Animation and Virtual Worlds","volume":"35 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142641927","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

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