Computer Animation and Virtual Worlds最新文献

Augmented reality (AR) is a technology that superimposes digital information onto the real world. As one of the three major forms of AR, spatial augmented reality (SAR) projects virtual content into public spaces, making it accessible to collaborators. Due to its shared large display area, SAR has significant potential for application in collaborative design. However, existing SAR interaction methods may suffer from inefficiencies and poor collaborative experiences. To address this issue, CoPadSAR, a Pad-based cross-device interaction method, is proposed. It can map 2D operations from each Pad onto 3D objects within the SAR environment, allowing users to collaborate using multiple Pads. Moreover, a prototype is presented that supports collaborative painting, annotation, and object creation. Furthermore, a comparative study involving 40 participants (20 pairs) is conducted. The results indicate CoPadSAR reveals better group performance than controller-based, gesture, and tangible interactions. It has greater usability and provides a better collaborative experience. The interviews further confirm the user preference for it. This study contributes to expanding the application of SAR in collaborative design.

Accurate 3D interacting hand mesh reconstruction from RGB images is crucial for applications such as robotics, augmented reality (AR), and virtual reality (VR). Especially in the field of robotics, accurate interacting hand mesh reconstruction can significantly improve the accuracy and naturalness of human-robot interaction. This task requires an accurate understanding of complex interactions between two hands and ensuring reasonable alignment of the hand mesh with the image. Recent Transformer-based methods directly utilize the features of the two hands as input tokens, ignoring the correlation between local and global features of the interacting hands, leading to hand ambiguity, self-occlusion, and self-similarity problems. We propose LGNet, Local and Global Feature Adaptive Network, through separating the hand mesh reconstruction process into three stages: A joint stage for predicting hand joints; a mesh stage for predicting a rough hand mesh; and a refine stage for fine-tuning the mesh-image alignment using an offset mesh. LGNet enables high-quality fingertip-level mesh-image alignment, effectively models the spatial relationship between two hands, and supports real-time prediction. Comprehensive quantitative and qualitative evaluations on benchmark datasets reveal that LGNet surpasses existing methods in mesh accuracy and alignment accuracy, while also showcasing robust generalization performance in tests on in-the-wild images.

Chinese painting is the traditional way of painting in China, with distinctive artistic characteristics and a strong national style. Creating Chinese paintings is a complex and difficult process for non-experts, so utilizing computer-aided Chinese painting generation is a meaningful topic. In this paper, we propose a novel Chinese painting generation model, which can generate vivid Chinese paintings in a stroke-by-stroke manner. In contrast to previous neural renderers, we design a Chinese painting renderer that can generate two classic stroke types of Chinese painting (i.e., middle-tip stroke and side-tip stroke), without the aid of any neural network. To capture the subtle semantic representation from the input image, we design a semantic loss to compute the distance between the input image and the output Chinese painting. Experiments demonstrate that our method can generate vivid and elegant Chinese paintings.

Accurate 3D craniofacial landmark detection is critical for applications in medicine and computer animation, yet remains challenging due to the complex geometry of craniofacial structures. In this work, we propose a coarse-to-fine framework for anatomical landmark localization on 3D craniofacial models. First, we introduce a Diffused Two-Stream Network (DTS-Net) for heatmap regression, which effectively captures both local and global geometric features by integrating pointwise scalar flow, tangent space vector flow, and spectral features in the Laplace-Beltrami space. This design enables robust representation of complex anatomical structures. Second, we propose a heat kernel-based energy optimization method to extract landmark coordinates from the predicted heatmaps. This approach exhibits strong performance across various geometric regions, including boundaries, flat surfaces, and high-curvature areas, ensuring accurate and consistent localization. Our method achieves state-of-the-art results on both a 3D cranial dataset and the BU-3DFE facial dataset.

We propose a novel simulation method for surface tension effects based on the Smoothed Particle Hydrodynamics framework, capturing versatile tension effects using a unified interface energy description. Guided by the principle of energy minimization, we compute the interface energy from multiple interfaces solely using the original kernel function estimation, which eliminates the dependence on second-order derivative discretization. Subsequently, we incorporate an inertia term into the energy function to strike a balance between tension effects and other forces. To simulate tension, we propose an energy diffusion-based method for minimizing the objective energy function. The particles at the interface are iteratively shifted from high-energy regions to low-energy regions through several iterations, thereby achieving global interface energy minimization. Furthermore, our approach incorporates surface tension parameters as variable quantities within the energy framework, enabling automatic resolution of tension spatial gradients without requiring explicit computation of interfacial gradients. Experimental results demonstrate that our method effectively captures the wetting, capillary, and Marangoni effects, showcasing significant improvements in both the accuracy and stability of tension simulation.

Micro-expressions (MEs) are spontaneous and transient facial movements that reflect real internal emotions and have been widely applied in various fields. Recent deep learning-based methods have been rapidly developing in micro-expression recognition (MER).Still, it is typical to focus on the one-sided nature of MEs, covering only representational features or low-ranking Action Unit (AU) features. The subtle changes in MEs characterize its feature representation weak and inconspicuous, making it tough to analyze MEs only from a single piece or a small amount of information to achieve a considerable recognition effect. In addition, the lower-order information can only distinguish MEs from a single low-dimensional perspective and neglects the potential of corresponding MEs and AU combinations to each other. To address these issues, we first explore how the higher-order relations of different AU combinations correspond with MEs through statistical analysis. Afterward, based on this attribute, we propose an end-to-end multi-stream model that integrates global feature learning and local muscle movement representation guided by AU semantic information. The comparative experiments were performed on benchmark datasets, with better performance than the state-of-art methods. Also, the ablation experiments demonstrate the necessity of our model to introduce the information of AU and its relationship to MER.

Elderly Computer Anxiety (ECA) often arises when older adults use digital tools, as it is a psychological condition influenced by both cognitive and functional impairments. Despite the widespread application of immersive virtual reality (IVR) technologies in enhancing cognitive abilities, their potential for addressing ECA remains underexplored. This study proposes an Immersive Virtual Reality Training (IVRT) model grounded in user experience(UX) design to comprehensively alleviate computer anxiety among older adults. The model integrates four interconnected modules: experience, training, socialization, and interaction. Each module is tailored to older adults, leveraging UX principles to enhance usability, foster engagement, and facilitate practical application through immersive VR. This paper conducted a randomized controlled trial involving 80 elderly individuals aged 65 to 85 to validate its effectiveness. Results showed significant reductions in anxiety levels within the IVRT group: Technophobia Scale (TS) scores decreased by an average of 0.49 points, and Gerontological Computer Anxiety Scale (GCAS) scores decreased by an average of 52.68 points, representing a 12.7% reduction (p < 0.05). These findings demonstrate that the IVRT model is significantly effective in reducing computer anxiety. It provides a solid foundation for developing immersive intervention measures tailored to older adults, better supporting their adaptation to the digital age.