Computer Animation and Virtual Worlds最新文献

Emergency plan training is very important for subway staff to respond quickly in the event of a fire emergency in a subway station. Compared to traditional training methods, immersive virtual reality serious games (IVR SGs) are gradually attracting attention. However, there are few studies currently on the application of this training method to fire emergency plan training for subway staff. Therefore, an IVR SG for subway intern staff fire emergency plan training was designed and developed. Firstly, the prototype development process for this IVR SG was described, then the effectiveness of the developed IVR SG training was validated through being compared with traditional written text training from the perspectives of knowledge acquisition and retention, self-efficacy improvement, and users' virtual reality (VR) experience. The results showed that the developed IVR SG was effective in terms of knowledge acquisition and self-efficacy improvement, and was more effective than the traditional written text training in short-term and long-term training effectiveness. In addition, the VR experience that the developed IVR SG provided to users was also acceptable. The study can provide some insights for the prototype development and validation of effectiveness on IVR SG for the emergency plan training.

Interactive computer technology is deeply integrated into traditional teaching methods. The traditional teaching of physics experiments in secondary schools suffers from the inability of teachers to provide timely guidance to students, the difficulty of controlling experimental variables, and the lack of uniformity in evaluation criteria. To address the aforementioned issues, we have developed an innovative system to improve secondary school physics education using computer vision-based interaction with virtual humans and sensors. The proposed system captures experimental data in real time so that student performance can be accurately monitored and assessed. Teachers can effortlessly configure experiments through simple coding, while the system leverages a multimodal macrolanguage model to offer contextual feedback and guidance. The system generates a virtual teacher that offers step-by-step guidance and real-time feedback. Usability tests indicate that the system significantly improves student engagement and comprehension of complex physics concepts, highlighting its potential to transform traditional science education. The advantage of real-time assessment and guidance in secondary school physics experiments is that it enables students to grasp abstract concepts in a more intuitive and comprehensible manner.

Effective audiovisual cueing can significantly enhance learners' attention to educational resources in the Virtual Reality (VR). However, predicting the impact of multimodal cueing on learners' attention in immersive teaching environments remains a challenging task. To address this, we propose a deep learning model named Attention Prediction Model (APM). This model employs RevFCN to extract visual and auditory cue features and incorporates a tailored Upsample-Aggregation Fusion Module (UAFM) to integrate multimodal representations. Additionally, an SANet is introduced to effectively combine the advantages of spatial and channel attention. Trained on our constructed dataset, APM achieved an attention prediction accuracy of 81.6%. These findings offer both theoretical and practical implications for the application of multimodal cueing in VR-based instructional design.

The vision of Industry 5.0, which addresses humanistic, adaptable and sustainable method, has evolved rapidly, particularly in digital twin-driven (DT) utilization in industry, for example, BMW factory and Tata steel. Nevertheless, it is no secret that the development of DT-powered virtual reality (VR) environments is hard work, which denotes there is untapped potential. Thus, a reusable DT model is required to speed up the creation and scaling of DT-driven VR environments. The objective of this study is for seeking solutions to make DT-driven VR contexts faster in an industrial environment. In particular, this study presents a way to develop the settings more effectively. The highlight of this study, and related industrial instances, is the development of safety education for human-robot collaboration in manufacturing contexts utilizing DT-driven VR settings. A formwork mode is introduced, which involves specific formworks and the entire framework for disposing the formworks in order to more quickly amend DT-driven VR environments to satisfy the requirements of a particular instance. The formwork is established applying two different industrial instances from manufacturing scenario.

Comprehensive observation of target area with pedestrians through pan-tilt-zoom (PTZ) camera networks is crucial in various surveillance applications. However, the dynamic configuration of PTZ cameras increases the difficulty of coordinating multiple cameras to monitor large-scale scenes. Since coverage control in PTZ camera networks has been proven to be an NP-hard problem, many studies have adopted virtual potential field (VPF) algorithms to efficiently obtain approximate solutions. The VPF methods treat camera viewpoints as charged particles. Through repulsive forces between these particles, PTZ camera networks expand scene coverage and reduce overlap between camera fields of view (FoVs). However, VPF-based methods cannot leverage the scene layout and target priority information, failing to cover pedestrians and other critical areas. In this work, we introduce a unified perception quality measure framework that quantifies surveillance importance for scenes, cameras, and pedestrians. Building on this framework, we design a coverage control scheme using a perceptive quality-based virtual potential field. This scheme models target regions and pedestrian priorities as virtual gravitational and attractive forces. It maximizes coverage of key regions, minimizes camera overlap, and supports high-resolution monitoring and tracking of pedestrians. Extensive experiments show that our approach outperforms state-of-the-art methods, achieving superior scene and pedestrian coverage performance.

Dance generation is a significant research area in computer arts and artificial intelligence. This study proposes a novel framework to enhance dance controllability and personalization through multimodal and multi-granularity control. The framework establishes global choreographic control of long sequences via music and dance style factors, while accommodating local style variations. Simultaneously, it enables fine-grained local control using style, text, and temporal factors for motion refinement. We develop two cross-modal Transformers: the LS-M2D model merges music and dance style features for local style-controllable dance generation, and the LT-SM2D model integrates textual guidance with music and dance style features for time-constrained local control. Experimental results demonstrate enhanced motion quality, effective multi-granularity style control, and precise text-guided flexibility. This provides valuable technical support for personalized intelligent dance generation systems.

Advances in microelectronic components and high-speed networks have enabled the widespread application of virtual reality (VR) technology in education. However, insufficient attention to knowledge visualization in VR learning has resulted in disorganized knowledge structures, comprehension difficulties, and mismatches between user experience and learning achievement. Therefore, we propose a Knowledge Cube (KC)-based visualization method to standardize knowledge encoding in VR learning. During courseware development, the instructor defines discrete knowledge as Events, organizes them into Event Groups, and populates data into a KC model to generate VR courseware. In subsequent VR learning, when learners search for task-relevant knowledge using the provided retrieval method, the KC model presents the corresponding events within interactive scenarios according to its predefined structure. Comparative experiments on different knowledge visualization methods revealed that, in VR learning, the KC method outperforms other VR approaches in both learning performance and efficiency. This method effectively guided learners to focus on the learning content and optimized the knowledge encoding in VR learning. This provides an operational framework for knowledge encoding in VR courseware design and emphasizes the importance of supporting effective learning behaviors over merely pursuing immersion, presenting a new perspective for refining the design approach of VR courseware.

Sign language recognition (SLR) requires interpreting dynamic hand gestures with complex variations in shape, orientation, motion, and spatial configuration. Conventional models such as U-Net and ResNet offer strengths in segmentation and feature extraction, respectively, but face critical limitations. U-Net struggles with retaining fine spatial details in cluttered backgrounds and lacks temporal modeling, while ResNet can lose motion continuity and suffers from vanishing gradient issues in deeper architectures. To overcome these challenges, we propose the holistic sign language interpretation network (HSLIN), a novel deep learning framework tailored for Indian sign language (ISL) recognition. HSLIN incorporates three key innovations: Uniformed frame isolation and augmentation (UFIA) for standardized preprocessing and noise removal, synaptic gesture movement analysis (SGMA) for capturing detailed motion using keypoint detection and optical flow, and a hybrid architecture combining U-Net-based segmentation with an enhanced ResNet-TC50V2 backbone. The novelty lies in fusing spatial precision with deep temporal modeling through bottleneck layers and temporal convolutional layers (TCL), enabling the model to effectively learn gesture patterns over time. Experimental results on the ISL-CSLTR dataset demonstrate that the proposed method achieves an accuracy of 99.9%, a precision of 100%, recall of 99.9%, and an F1-score of 100% across 14 word-level sign classes. Furthermore, an ablation study confirms the critical role of each architectural component in achieving optimal performance. These outcomes clearly establish the robustness, efficiency, and uniqueness of the proposed HSLIN framework, positioning it as a powerful solution for real-world ISL recognition and communication accessibility for the deaf and hard-of-hearing community.

Generating diverse and realistic movements has long been a central challenge in computer graphics. Generative Adversarial Networks (GANs) remain a compelling solution due to their ability to perform well even with limited training data. However, traditional GANs generate samples directly, which can lead to the omission of certain data patterns. To address this limitation, we introduce SinMDGan, a hybrid deep learning framework for single-motion synthesis that leverages a Diffusion-GAN model. Our approach integrates the strengths of GANs, which capture global motion characteristics, with diffusion techniques, which refine local details, ensuring both authenticity and diversity in generated movements. Unlike conventional cascaded GANs, our framework employs a single generator-discriminator pair, utilizing different diffusion time steps to synthesize novel and diverse motions from a single short sequence. Experimental evaluations demonstrate the effectiveness of our model in achieving stable data distribution coverage and enhancing output diversity. Additionally, we showcase various applications, including motion composition and long-sequence generation, highlighting the versatility of our approach.