In order to be successfully executed, collaborative tasks performed by two agents often require a cooperative strategy to be learned. In this work, we propose a constraint-based multi-agent reinforcement learning approach called Constrained Multi-agent Soft Actor Critic (C-MSAC) to train control policies for simulated agents performing collaborative multi-phase tasks. Given a task with n phases, the first n-1 phases are treated as constraints for the final task phase objective, which is addressed with a centralized training and decentralized execution approach. We highlight our framework on a tray balancing task including two phases: tray lifting and cooperative tray control for target following. We evaluate our proposed approach and compare it against its unconstrained variant (MSAC). The performed comparisons show that C-MSAC leads to higher success rates, more robust control policies, and better generalization performance.

The particular characteristics of deaf-mutes make them more likely to have mental health problems. Due to their particular way of communication, it is more difficult for them to deal with mental health problems than ordinary people. Nowadays, those psychologists who are good at sign language are in short supply, and remote assistance cannot achieve satisfactory results. Therefore, a library of virtual emotional gestures based on electroencephalogram(EEG) was established and a prototype system for mental health examination of deaf-mutes was proposed, which help deaf-mutes identify their psychological problems in time and assist medical staff to examine the psychological problems encountered by deaf-mutes. In addition, the virtual library of emotional gestures is established with the assistance of the chief physician from a 3A hospital in Henan Province. More importantly, the later experiments demonstrate the applicability of this virtual system.

Virtual Reality (VR) and Augmented Reality (AR) applications are becoming increasingly prevalent. However, constructing realistic 3D hands, especially when two hands are interacting, from a single RGB image remains a major challenge due to severe mutual occlusion and the enormous diversity of hand poses. In this paper, we propose a Disturbing Graph Contrastive Learning strategy for two-hand 3D reconstruction. This involves a graph disturbance network designed to generate graph feature pairs to enhance the consistency of the two-hand pose features. A contrastive learning module leverages high-quality generative features for a strong feature expression. We further propose a similarity distinguish method to divide positive and negative features for accelerating the model convergence. Additionally, a multi-term loss is designed to balance the relation among the hand pose, the visual scale and the viewpoint position. Our model has achieved State-of-the-Art results in the InterHand2.6M benchmark. Ablation studies show the model’s great ability to correct unreasonable hand movements. In subjective assessments, our Graph Disturbance Learning method significantly improves the construction of realistic 3D hands, especially when two hands are interacting.

Desiccation cracking of soil-like materials is a common phenomenon in natural dry environment, however, it remains a challenge to model and simulate complicated multi-physical processes inside the porous structure. With the goal of tracking such physical evolution accurately, we propose an MPM based method to simulate volumetric shrinkage and crack during moisture diffusion. At the physical level, we introduce Richards equations to evolve the dynamic moisture field to model evaporation and diffusion in unsaturated soils, with which a elastoplastic model is established to simulate strength changes and volumetric shrinkage via a novel saturation-based hardening strategy during plastic treatment. At the algorithmic level, we develop an MPM-fashion numerical solver for the proposed physical model and achieve stable yet efficient simulation towards delicate deformation and fracture. At the geometric level, we propose a correlating stretching criteria and a saturation-aware extrapolation scheme to extend existing surface reconstruction for MPM, producing visual compelling soil appearance. Finally, we manifest realistic simulation results based on the proposed method with several challenging scenarios, which demonstrates usability and efficiency of our method.

A great number of virtual museums exhibit archaeological artefacts in an interactive form using 3D digital models to disseminate them to as many users as possible. Currently, 3D models obtained as a result of scanning museum objects have gained a great popularity. This technique allows to faithfully reproduce objects and to expose artefacts too delicate and precious to be presented in the real world. The aim of this paper is to create a virtual museum in the VR world using 3D models obtained by 3D scanning with structured light.  The advantages of optimising mesh models for their use in virtual exhibitions are discussed. Unity and Unreal Engine, were used to create virtual museums in the VR world. Two twin test applications were prepared. This allowed to compare the performance of the developed applications (processor, graphics card and RAM load). A survey was also conducted among the users of the implemented virtual museum application. For immersion in the world of VR, a low-cost solution was used, involving the use of cheap VR frames, a smartphone and a computer. The frames together with the smartphone were used to display the image, while the entire rendering process was performed on the computer.

In this paper, we propose an integrated model for simulating the interaction between crowds and fluid particles. Our focus is on simulating evacuation motion for crowds in the face of sudden floods. Our model treats both the crowd and the water as fluid particles, which allows us to incorporate various forces such as pressure, shear, buoyancy, and active forces to drive the agents. Additionally, we have designed a minimum rotational path-planning algorithm for agents to search for safe destinations during evacuations. To develop practical crowd evacuation strategies, we observed and studied survival techniques from whirlpools and sudden changes in water levels during floods. Our simulated evacuation results provide plausible strategies for crowds to survive dangerous floods.