Computer Animation and Virtual Worlds最新文献

In this article, we propose an AI-agent that integrates a large language model (LLM) with a retrieval-augmented generation (RAG) system to deliver reliable historical information from the Annals of the Joseon Dynasty through both objective facts and contextual analysis, achieving significant performance improvements over existing models. For an AI-agent using the Annals of the Joseon Dynasty to deliver reliable historical information, clear source citations and systematic analysis are essential. The Annals, an official record spanning 472 years (1392–1897), offer a dense, chronological account of daily events and state administration that shaped Korea's cultural, political, and social foundations. We propose integrating a LLM with a RAG system to generate highly accurate responses based on this extensive dataset. This approach provides both objective information about historical figures and events from specific periods and subjective contextual analysis of the era, helping users gain a broader understanding. Our experiments demonstrate improvements of approximately 23 to 50 points on a 100-point scale compared with the GPT-4o and OpenAI AI-Assistant v2 models.

Fruit growth is an interesting time-lapse process. The simulation of this process using computer graphics technology can have many applications in areas such as films, games, agriculture, etc. Although there are some methods to model the shape of the fruit, it is challenging to accurately simulate its growth process and include shape changes. We propose a botanical-based framework to address this problem. By combining the growth pattern function and the exponential model in botany, we propose a mesh scaling method that can accurately simulate the fruit volume increase. Specifically, the RGR (relative growth rate) in the exponential model is automatically calculated according to the user's input growth pattern function or real size data. In addition, we model and simulate fruit shape changes by integrating axial, longitudinal, and latitudinal shape parameters into the RGR function. Various defective fruits can be simulated by adjusting these parameters. Inspired by the principle of root curvature, we propose a deformation technique-based approach in conjunction with our volume increase approach to simulate the bending growth of fruits such as cucumber. Various experiments show that our framework can effectively simulate the growth process of a wide range of fruits with shape change or bending.

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.