The redirected walking (RDW) method for multi-user collaboration requires maintaining the relative position between users in a virtual environment (VE) and physical environment (PE). A chasing game in a VE is a typical virtual reality game that entails multi-user collaboration. When a user approaches and interacts with a target user in the VE, the user is expected to approach and interact with the target user in the corresponding PE as well. Existing methods of multi-user RDW mainly focus on obstacle avoidance, which does not account for the relative positional relationship between the users in both VE and PE. To enhance the user experience and facilitate potential interaction, this paper presents a novel dynamic alignment algorithm for multi-user collaborative redirected walking (DA-RDW) in a shared PE where the target user and other users are moving. This algorithm adopts improved artificial potential fields, where the repulsive force is a function of the relative position and velocity of the user with respect to dynamic obstacles. For the best alignment, this algorithm sets the alignment-guidance force in several cases and then converts it into a constrained optimization problem to obtain the optimal direction. Moreover, this algorithm introduces a potential interaction object selection strategy for a dynamically uncertain environment to speed up the subsequent alignment. To balance obstacle avoidance and alignment, this algorithm uses the dynamic weightings of the virtual and physical distances between users and the target to determine the resultant force vector. The efficacy of the proposed method was evaluated using a series of simulations and live-user experiments. The experimental results demonstrate that our novel dynamic alignment method for multi-user collaborative redirected walking can reduce the distance error in both VE and PE to improve alignment with fewer collisions.

In recent years, multi-user collaboration has emerged as a trend in virtual reality. However, the existing redirected walking methods for multi-user VR scenarios usually are devoted to the obstacle avoidance, neglecting the users’ relative positions in both virtual environment(VE) and physical environments (PE), as well as the natural visual relationships. In order to enhance the collaborative experience, this paper presents a novel Alignment-based Redirected Walking for Multi-User VR scenarios, Align-RDW. Our algorithm will dynamically selects an optimal moving target for the current user’s alignment. Our approach integrates concepts such as user state loss and dynamic guiding points, applying auxiliary forces to enhance user alignment performance. Simultaneously, we utilize the calculated optimal deviation angle to adjust the user’s orientation. We evaluated the algorithm through simulations and Live-User experiments. The results demonstrate that our strategy achieves a longer reset distance, as well as smaller alignment distance errors and angle errors than others.

A ReID-based multi-object tracking (MOT) network with adjacent frame enhancement is proposed, which is constructed with center-based detection network. To enrich the ReID features, our method treats the adjacent frame as data augmentation of the current frame, implicitly uses the network to achieve data alignment for each object at different frames, and then uses the aligned adjacent features to enhance the ReID. In the training phase, the method adds a location prediction branch to guide the network to learn the feature correspondence of the same object in two frames. In the inference phase, our method uses historical tracking to construct a heatmap of interest of the adjacent frame to inform the network of the locations of all objects in adjacent frames, then the network autonomously mines the features of each object in these two frames. Our approach performs better in MOT challenges than the existing multi-object tracking network.

Redirected walking (RDW) algorithms subtly manipulates users’ movements in virtual environments through three gains, distorting their trajectories in the physical space, thus allowing them to explore infinite virtual environments within limited physical spaces. However, collisions with physical boundaries or obstacles in physical spaces are inevitable. At this point, explicit redirected techniques, such as reset, are needed to pause users’ roaming experience and redirect them to open areas. Currently, most reset strategies wait until a collision occurs before resetting the user, while these reset algorithms can achieve certain effectiveness in simple physical environments. However, in complex physical environments, users may walk into narrow areas before collisions occur. Therefore, no matter which direction they turn, further collisions are likely to happen over short distances. To enhance obstacle avoidance capability while adapting to various complex physical layouts, this paper adopts the concept of pre-reset and presents a novel method of intelligent pre-reset IPR for redirected walking through reinforcement learning, which determines whether an early reset should be performed and the optimal reset direction based on the current physical environment, as well as the user’s position and walking trajectory. Through simulation and human experiments compared with other reset algorithms in several environments, we demonstrate the superiority of our approach, achieving higher average walking distances between resets while adapting to various nuances of reset techniques. At the same time, IPR is applicable to various RDW algorithms and environments of different complexities.