In immersive VR applications, avatars shape users' identities and social presence. Personalization requires generating realistic outfits for avatars, often specified through a single reference image, while supporting seamless editing, adaptation to diverse avatars, and efficient rendering. Achieving these goals is challenging because VR avatars, even humanoid ones, exhibit substantial variations in body shapes and topologies. This inherent diversity makes it difficult to collect sufficient paired data, impeding the evolution of generalizable end-to-end image-to-garment models. To this end, we propose Tailor, a two-stage framework that dresses 3D humanoid avatars from a single reference image while preserving the wearing effects observed in the image. In the first stage, Tailor leverages a structured garment representation based on sewing patterns, enabling the network to predict garments in a low-dimensional, interpretable, and topology-independent space. In the second stage, Tailor performs instance-specific optimization to adapt the predicted sewing pattern to the avatar, ensuring consistent wearing effects across varying avatars. Furthermore, this framework also enables seamless garment editing, on-the-fly adaptation, and real-time rendering, making it particularly suitable for large-scale VR environments. Extensive experiments demonstrate that Tailor achieves results comparable to professional manual designs and produces garments that are both visually appealing and better aligned with reference styles than those generated by naive pattern-scaling baselines, as validated through human perceptual studies.

Emerging VR applications have revolutionized user experiences by immersing individuals in digitally crafted environments. However, fully immersive experiences introduce new challenges, notably the risk of physical hazards when users are unaware of their surroundings. Existing solutions, including guardian spaces and locomotion systems, present trade-offs that either disrupt the immersive experience or risk inducing motion sickness. To address these challenges, we propose a novel approach that dynamically rearranges VR scenes according to users' physical spaces, seamlessly embedding physical constraints and interaction tasks into the virtual environment. We design a computational model to optimize the rearranged scene through a cost function, ensuring collision-free interactions while maintaining visual fidelity and the goal of interaction tasks. The experiments demonstrate improvements in user experience and safety, presenting an innovative solution to harmonize physical and virtual environments in VR applications.

Like real humans, virtual characters also need to dress up according to different application scenarios so that the virtual character appears professionally, harmoniously, and naturally. However, manual selection is tedious, and the appearances of virtual characters usually lack variety. In this paper, we propose a new problem of synthesizing appropriate dress for a virtual character based on the scenario analysis where he/she shows up. We come up with a pipeline to tackle the scenario-aware dress synthesis problem. Firstly, given a scene, our approach predicts a dress code from the extracted high-level information in the scene, consisting of season, occasion, and scene category. Then our approach tunes the dress details to fit the aesthetic criteria and the virtual character's attributes. An optimization of a cost function implements the tuning process. We carried out experiments to validate the efficacy of the proposed approach. The perceptual study results show the good performance of our approach.

In this paper, we aim at guiding people to accomplish a personalized task, work surface organizing, in mixed reality environment, which can also be applied to intelligent robots. Through the cameras mounted in a MR device, e.g., Hololens, we firstly capture a person's daily activities in real scene when he uses the work surface. From such activities, we model the individual behavior habits and apply them to optimize the arrangement of the work surface. A cost function is defined for the optimization, considering general arrangement rules and human habitual behavior. The optimized arrangement is suggested to the user by augmenting the virtual arrangement on the real scene. To evaluate the effectiveness of our approach, we conducted experiments on a variety of scenes.

In this paper, we propose a novel optimization framework to synthesize an aesthetic pose for the virtual character with respect to the presented user's pose. Our approach applies aesthetic evaluation that exploits fully connected neural networks trained on example images. The aesthetic pose of the virtual character is obtained by optimizing a cost function that guides the rotation of each body joint angles. In our experiments, we demonstrate the proposed approach can synthesize poses for virtual characters according to user pose inputs. We also conducted objective and subjective experiments of the synthesized results to validate the efficacy of our approach.

The issue of sparsity adaptive channel reconstruction in time‐varying cooperative communication networks through the amplify‐and‐forward transmission scheme is studied. A new sparsity adaptive system identification method is proposed, namely reweighted norm ( ) penalised least mean square (LMS) algorithm. The main idea of the algorithm is to add a norm penalty of sparsity into the cost function of the LMS algorithm. By doing so, the weight factor becomes a balance parameter of the associated norm adaptive sparse system identification. Subsequently, the steady state of the coefficient misalignment vector is derived theoretically, with a performance upper bounds provided which serve as a sufficient condition for the LMS channel estimation of the precise reweighted norm. With the upper bounds, the authors prove that the ( ) norm sparsity inducing cost function is superior to the reweighted norm. An optimal selection of p for the norm problem is studied to recover various d sparse channel vectors. Several experiments verify that the simulation results agree well with the theoretical analysis, and thus demonstrate that the proposed algorithm has a better convergence speed and better steady‐state behaviour than other LMS algorithms.

The functionality of a workspace is one of the most important considerations in both virtual world design and interior design. To offer appropriate functionality to the user, designers usually take some general rules into account, e.g., general workflow and average stature of users, which are summarized from the population statistics. Yet, such general rules cannot reflect the personal preferences of a single individual, which vary from person to person. In this paper, we intend to optimize a functional workspace according to the personal preferences of the specific individual who will use it. We come up with an approach to learn the individual's personal preferences from his activities while using a virtual version of the workspace via virtual reality devices. Then, we construct a cost function, which incorporates personal preferences, spatial constraints, pose assessments, and visual field. At last, the cost function is optimized to achieve an optimal layout. To evaluate the approach, we experimented with different settings. The results of the user study show that the workspaces updated in this way better fit the users.

Multicast technology will play a very important role in the future multimedia communication application. In this paper, a new transmission mechanism called multi-group multicast (MGMC) based on inter-group and inner-group cooperation is proposed for cognitive networks. The new transmission mechanism sets multiply multicast groups as a pair, and then these multicast groups transmit information in a cooperative way by using the same frequency resource. We formulate a weighted overall rate optimization problem with interference constraints to the primary user (PU) and peak power constraint at each user and obtain the optimal solution by theoretical analysis in this new transmission model. Numerical results show the impact of user number on rate for every multicast group.

This study investigates sensing‐based spectrum sharing access (SSSA) and sensing‐based spectrum opportunistic access (SSOA) schemes in cooperative orthogonal frequency division multiplexing (OFDM)‐based cognitive radio networks with imperfect spectrum sensing. The optimal resource allocation strategy, including sensing time and transmit power, is designed to maximise the ergodic throughput of the secondary system. For a two‐hop cooperative communication in the secondary system, the authors adopt the amplify‐and‐forward relay protocol and enable the source and relay to sense the state of the primary user jointly. To protect the PU effectively from harmful interference, they consider the average interference power constraint in each hop. The total average transmit power constraint of the source and relay is considered. Two simplified versions for the SSSA and SSOA schemes are employed because of the complexity of the problem. They then propose two algorithms that acquire the optimal sensing time and power allocation for both schemes. Finally, simulation results are presented to compare the performance of the two schemes.

In metro Passenger Information Systems (PISs), frequent train handoffs can cause severe video distortion. In this paper, we take an integrated design approach to jointly optimize application layer parameters and handoff decisions to improve video transmission quality over PISs. We present a train-ground video communication network based on fountain codes and IEEE 802.11p for metro PISs. The handoff decision and application layer parameters adaptation problem is formulated as a stochastic semi-Markov Decision Process (SMDP). Minimizing the end-to-end total video distortion is the objective in our model. Simulation results show that the proposed SMDP based optimization algorithm can significantly improve the end-to-end video transmission quality in metro PISs.

Train-ground communication technology is one of the key technologies for communication-based train control (CBTC), which is an automated train control system for railways that ensures safe operation of rail vehicles using data communications. In CBTC systems, less service availability could cause train derailment, collision or even catastrophic loss of lives or assets. Therefore, the availability of train-ground communication should be carefully considered in designing CBTC systems. In this paper, we propose two WLAN-based train-ground communication schemes with redundancy to improve the availability in CBTC systems. The availability is analyzed using continuous time Markov chain (CTMC) model. Numerical examples illustrate that the proposed schemes with redundancy can significantly improve the availability of WLAN-based train-ground communication in CBTC systems.

Communication-based train control (CBTC) system is an automated control system for railways that ensures the safe operation of rail vehicles using wireless data communications. The train-ground communication system is one of the key subsystem of CBTC. CBTC systems have stringent requirements for wireless communication availability and latency. In this paper, we propose a seamless handoff scheme based on stream control transmission protocol (SCTP) and IEEE 802.11p to provide high link availability for train-ground communication systems in CBTC. Moreover, we propose a handoff decision policy to improve SCTP throughput and achieve seamless handoff in train-ground communications systems. Simulation results illustrate the effectiveness of the proposed scheme.