Computer Animation and Virtual Worlds最新文献

High-fidelity 3D face morphing aims to achieve seamless transitions between realistic 3D facial representations of different identities. Although 3D Gaussian Splatting (3DGS) excels in high-quality rendering, its application to morphing is hindered by the lack of Gaussian primitive correspondence and variations in primitive quantities. To address this, we propose GSFaceMorpher, which is a novel framework for high-fidelity 3D face morphing based on 3DGS. Our method constructs an auxiliary model that bridges the source and target face models by aligning the geometry through Radial Basis Function (RBF) warping and optimizing the appearance in the image space. This auxiliary model enables smooth parameter interpolation, whereas a diffusion-based refinement step enhances critical facial details through attention replacement from the reference faces. Experiments demonstrate that our method produces visually coherent and high-fidelity morphing sequences, significantly outperforming NeRF-based baselines in terms of both quantitative metrics and user preferences. Our work establishes a new benchmark for high-fidelity 3D face morphing with applications in visual effects, animation, and immersive experiences.

This paper presents a control simulation that represents user-desired shapes using multiple connected soap bubbles. A previous method attempted to control a single soap bubble using external forces. However, due to the strong surface tension making spherical babbles, elongated shapes could not be achieved. To address this issue, this paper aims to develop a control simulation that achieves diverse soap bubble shapes by dividing the target shape into connected soap bubbles. In our approach, we first generate an initial soap bubble configuration composed of multiple bubbles to represent the target shape. Then, by applying external forces to each bubble, we simulate the bubbles to maintain their shape along the target form. We use an implicit-function-like representation for the connected soap bubbles and develop a new polygonizer that makes shapes including the internal faces of bubbles. By demonstrating examples with various target shapes such as objects and text, we show the effectiveness of our proposed control method.

Large Language Models (LLMs) offer almost immediate human-like quality responses to user queries. Conversational agent systems support natural language dialogues utilizing LLM backends in combination with Text-to-Speech (TTS) and Automatic Speech Recognition (ASR) technologies, enabling life-like characters in virtual environments. This study investigates the relationship between user personality and perceived agent personality in LLM-based natural language dialogue. We adopt a Virtual Reality (VR) setting where the user can talk with the agent that assumes the role of Socrates, the famous philosopher. To this end, we utilize a three-dimensional (3D) avatar model resembling Socrates and use specific LLM prompts to get stylistic answers from OpenAI's Chat Completions Application Programming Interface (API). Our user study measures the agent's personality and the system's ease of use, quality, realism, and immersion concerning the user's self-reported personality. The results suggest that the user's conscientiousness, extraversion, and emotional stability have a moderate effect on certain personality factors and system qualities. User conscientiousness affects the perceived ease of use, quality, and realism, while user extraversion affects perceived agent conscientiousness, system realism, and immersion. Additionally, the user's emotional stability correlates with perceived extraversion and agreeableness.

Sampled point clouds, particularly with prelabeled annotations and ground truth metrics, are frequently used in computer graphics and machine learning. In this work, we focus on a fuzzy sampling approach for such point clouds with qualified uniformity properties. After abstracting the uniformity requirements, a novel approach to sampling point clouds from implicitly defined curves/surfaces is proposed. The approach deliberately combines techniques including isodeviation dispatch, curvature compensation, and normalized distance blue noise. The experimental results show various sampled point clouds with uniform visual effects and statistical metrics. Moreover, the comparisons in terms of distance, density, and thickness uniformity with state-of-the-art methods exhibit the approach's advantages. Due to its low cost, ground truth, and annotation easiness features, the method will be smoothly applied in deep learning and computer animation.

As quintessential cultural symbols in traditional shadow puppetry, artistic motifs encapsulate profound historical narratives and serve as vital conduits for intangible cultural heritage preservation. However, this craft confronts existential threats from digital entertainment proliferation and practitioner attrition. To address these challenges, this study proposes CSPMotifsGAN, an enhanced CycleGAN framework for constructing a motif data set through three-stage processing: adaptive denoising, hierarchical classification, and multi-branch feature extraction (contour, texture, color). By integrating adversarial loss, cycle-consistency loss, and identity preservation loss, the model effectively resolves color distortion and textural degradation inherent in conventional CycleGAN. Experimental results demonstrate significant improvements: Fréchet Inception Distance (FID), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM), validated through both subjective evaluations and statistical analysis.

This study explores the use of virtual reality (VR) in upper limb rehabilitation, comparing bionic and non-bionic control strategies. While VR shows potential for immersive, engaging rehabilitation, recent findings question the effectiveness of prioritizing user rehabilitation needs over bionic strategies. Few studies have examined the impact of these strategies on motor performance, motivation, and engagement. To address this, we designed two hand motion control systems—bionic and non-bionic—using a virtual block test. The results show that the bionic control strategy improves early training experiences and motor performance, but with practice, the non-bionic control group demonstrates greater adaptability, motor flexibility, and learning efficiency. This suggests that while bionic strategies may be beneficial in early stages, arbitrary control systems offer better long-term outcomes.

Engineering education faces challenges in effectively conveying complex theoretical knowledge and practical skills. Traditional teaching tools like smart boards, education kits, web pages, and computer-based simulators often fall short in bridging the gap between theory and hands-on application. Augmented reality (AR) has emerged as a promising solution to fill this educational gap by providing immersive and interactive learning experiences. In this study, an AR-based application has been developed for operational amplifiers. Agile methodology and the ADDIE learning development model were used in the system development and instructional design of this application. This application, which requires only a smartphone and a breadboard, detects the circuit marker when users point their smartphone camera at it. After detection, the user can interact with the interface to choose whether the system shows input–output signals for different component values, the output voltage formula, a 3D model, or a brief lecture about the used amplifier. These virtual elements are overlaid onto the real-world breadboard based on the user's selection. This integration of AR technology provides an immersive, interactive learning experience, allowing students to visualize and interact with circuit elements in real time.

Current AI animation generation methods excel in tight-fitting clothing scenarios but struggle with deformation distortion and the gradual loss of wrinkles over extended simulations in loose-fitting clothing. To address these issues, we propose a multi-attribute-aware Graph Network. This approach mitigates the gradual loss of wrinkles by dividing animation sequences into multiple stages based on motion categories, recognizing that identical body postures can cause different clothing deformations due to varying motion tendencies. In each stage, we first restore coarse, globally guided deformations based on the motion category, followed by enhancing detailed features. We observed that garments within the same sport category exhibit similar local wrinkles and that the degree of fit to the body varies significantly across different regions of the same garment. We introduce two specific clothing attributes: “looseness” and “deformity,” which relate to local wrinkles and have physical significance. A clothing attribute encoder perceives these attributes and constructs a clothing graph model to estimate detailed features. Our method effectively handles clothing deformations across various motion types, including extreme postures, with qualitative and quantitative analyses confirming its effectiveness.