Computer Animation and Virtual Worlds最新文献

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.

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.

Recent advances in Score Distillation Sampling (SDS) have enabled text-driven 3D human generation, yet the standard classifier-free guidance (CFG) framework struggles with semantic misalignment and texture oversaturation due to limited model capacity. We propose a novel framework that decouples conditional and unconditional guidance via a dual-model strategy: A pretrained diffusion model ensures geometric stability, while a preference-tuned latent reward model enhances semantic fidelity. To further refine noise estimation, we introduce a lightweight U-shaped Swin Transformer (U-Swin) that regularizes predicted noise against the reward model, reducing gradient bias and local artifacts. Additionally, we design a time-varying noise weighting mechanism to dynamically balance the two guidance signals during denoising, improving stability and texture realism. Extensive experiments show that our method significantly improves alignment with textual descriptions, enhances texture details, and outperforms state-of-the-art baselines in both visual quality and semantic consistency.

This study explores how environmental design elements in library spaces influence human psychophysiological responses using virtual reality (VR). Thirty participants experienced VR simulations of library reading areas, with variations in wall color, flooring material, and lighting intensity, whereas electroencephalography (EEG) and galvanic skin response (GSR) measured physiological reactions alongside subjective ratings. Moderate lighting (20,000–30,000 cd) minimized arousal and supported attention, while white walls enhanced relaxation via increased alpha brain activity. Green plant walls slightly boosted attention-related beta activity, and wood flooring was rated highest for comfort and naturalness. VR enabled precise control of design variables, advancing environmental psychology research. These findings offer evidence-based guidelines for designing public spaces like libraries to enhance user well-being and cognitive performance, with implications for educational and public buildings.