ABSTRACT Static furniture cannot meet the requirements of multifunctional layout design. Although autonomously reconfigurable furniture presents a viable solution, current systems demonstrate limited capability in coordinating large‐scale multi‐furniture movements within complex indoor settings. To address these challenges, we present a hierarchical layout transformation system featuring: (1) A context‐aware navigation strategy that integrates adaptive navigation radius optimization and orientation‐aware rotation control, enabling efficient and safe maneuvering of large‐scale furniture in indoor environments; and (2) the Multi‐Agent Furniture Coordination (MAFC) algorithm with dynamic priority adjustment and deadlock resolution, ensuring reliable and collision‐free multi‐furniture movement. Experimental results show improved performance over traditional navigation‐based approaches in terms of coordination reliability and path adaptability. Compared to state‐of‐the‐art methods, our system demonstrates enhanced capability in managing large‐scale furniture movement under spatial constraints, enabling robust layout transformations for smart indoor environments.

ABSTRACT 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.

ABSTRACT The COVID‐19 pandemic has profoundly disrupted traditional face‐to‐face education, accelerating the shift towards digital learning environments and prompting the adoption of innovative pedagogical approaches. Among these, Augmented Reality (AR) has emerged as a transformative educational tool, offering immersive and interactive platforms that enhance students' understanding of complex concepts. This study investigates the integration of AR technology into university‐level beverage preparation courses, with the aim of fostering creativity and strengthening professional competencies. The findings indicate that AR has considerable potential to enhance creative performance, reshape pedagogical practices, and promote self‐directed learning. By addressing prevalent learning challenges and providing actionable insights into students' developmental progress, AR supports the design of tailored teaching strategies while cultivating industry‐relevant skills. This research highlights the role of AR in advancing inclusive, equitable, and high‐quality education, in alignment with Sustainable Development Goal 4 (SDG 4), and underscores its broader significance in redefining educational paradigms in the post‐pandemic era.

ABSTRACT Reliable estimation of torso direction is essential for virtual locomotion as it controls the locomotion direction in virtual environments. Existing approaches rely on external sensors to track torso direction or use the poses and/or movements from the head, hands, or feet to control the direction of travel. In addition, deep learning methods based on a head‐mounted display (HMD) and its controllers were proposed for torso direction estimation but are unable to produce continuous estimates or that the estimation errors were not systematically evaluated. On the other hand, 3D full‐body human motion generation methods based on an HMD and its controllers are capable of estimating torso direction but this capability was largely ignored for its use in virtual steering. We proposed a new method called TCNPoser that can perform torso direction estimation and generate 3D full‐body human motion. Through offline evaluation and a user study, we show that the proposed method achieves the state‐of‐the‐art (SOTA) performance for real‐time torso direction estimation.

ABSTRACT In the ever‐evolving world of gaming, controller‐based input is quickly becoming obsolete. Various applications, including virtual reality (VR) and augmented reality (AR) games, have used motion‐ and gesture‐controlled video game consoles. The current state of the art relies on depth images of the hand that do not utilize color information from the RGB spectrum. In this work, we focus on the development of an interactive VR game that utilizes hand pose recognition from the RGB domain to increase user experience, but also simplify the functionality of the game. To address this challenge, a 3D multi‐user VR game themed around a “tennis match” was developed using the Unity engine. We also investigate whether we can estimate the coordinates of colored objects connected to the hand movement of the players and track human gestures to navigate through the game functions in real time using an RGB camera. Statistical analysis showed that the user experience increased concerning engagement and satisfaction using a more natural form of control that allows players to focus on the excitement of the game without worrying about button presses or joystick movements. Such hand pose recognition systems can be implemented to replace the traditional controller‐based entry systems used today.

ABSTRACTThis study presents a novel tri‐manual interaction framework that enables users to control two physical hands via VR controllers and a virtual robotic arm through a hybrid brain‐computer interface (32‐channel EEG and eye‐tracking). The virtual robotic arm, implemented as a 6‐DOF industrial manipulator in Unity, is controlled through simplified BCI commands: forward/backward movement along the z‐axis based on motor imagery strength, with automatic grasping triggered by sustained attention thresholds. Twenty‐five participants completed 30 trials, each following a 60‐s protocol with five phases: rest, target presentation, preparation, execution, and feedback. Results demonstrated that the hybrid BCI system achieved superior performance compared to EEG‐only control: 8.5% improvement in task success rate, 34.5% increase in positioning accuracy, and 46.2% reduction in cognitive load. The CNN‐LSTM architecture achieved 86.3% motor imagery classification accuracy. Learning effects were observed within trials, with performance plateauing after 3.8 ± 0.7 attempts. Time‐frequency analysis revealed hierarchical neural coordination mechanisms underlying the tri‐manual control. This research validates the feasibility of augmented manipulation in virtual environments, establishing a foundation for advanced human‐robot collaboration in animation and VR applications.

ABSTRACTMost of point cloud segmentation methods are based on clean datasets and are easily affected by label noise. We present a novel method called Joint‐learning, which is the first attempt to apply a dual‐network framework to point cloud segmentation with noisy labels. Two networks are trained simultaneously, and each network selects clean samples to update its peer network. The communication between two networks is able to exchange the knowledge they learned, possessing good robustness and generalization ability. Subsequently, adaptive sample selection is proposed to maximize the learning capacity. When the accuracies of both networks are no longer improving, the selection rate is reduced, which results in cleaner selected samples. To further reduce the impact of noisy labels, for unselected samples, we provide a joint label correction algorithm to rectify their labels via two networks' predictions. We conduct various experiments on S3DIS and ScanNet‐v2 datasets under different types and rates of noises. Both quantitative and qualitative results verify the reasonableness and effectiveness of the proposed method. By comparison, our method is substantially superior to the state‐of‐the‐art methods and achieves the best results in all noise settings. The average performance improvement is more than 7.43%, with a maximum of 11.42%.

ABSTRACTWe 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.