Virtual Reality Animation Interaction Design using Bayesian Physics-Informed Neural Network with Archimedes Optimization Algorithm Based Scene Modelling

Abstract

Virtual Reality Animation Interaction Design is a dynamic field at the intersection of technology and creativity, where immersive experiences come to life through the fusion of animation and interactive design. There are some challenges involves in design of virtual Reality animation. The main challenge is animation image error involves in the design. To overcome this issue, present a Virtual Reality Animation Interaction Design Using Bayesian Physics-Informed Neural Network with Archimedes Optimization Algorithm Based scene modelling (VRAID-BPINN-AOA) is proposed. Initially, the animation images are collected from animation dataset. Then, the animation images are fed to pre-processing segment. In pre-processing segment, the noise of the image is removing layer by layer by utilizing adaptive-noise Augmented Kalman Filter (ANAKF). Then the pre-processed animation image is given for Feature extraction process. In Feature extraction, spatiotemporal features such as Object Position, velocity, Optical flow and Crowd Density are extracted by utilizing Multi-Level 2-D Quantum Wavelet Transform (ML2DQWT). Finally the extracted feature attributes are given to Bayesian physics-informed neural network (BPINN) for the prediction of error in the animation images. In general, BPINN does not express some adaption of optimization strategies for determining optimal parameters to promise accurate prediction of error. Therefore, Archimedes Optimization Algorithm (AOA) is proposed to optimize the parameter of BPINN .The proposed technique is implemented and efficacy of VRAID-BPINN-AOA technique is assessed by support of numerous performances such as Bit Error Rate, Design Rate, End to End Delay, Latency, and Transmission Rate. Proposed VRAID-BPINN-AOA method attains 28.56%, 26.67% and 25.67% lower Bit error rate and26.42%, 25.67% and 23.67% lower Design rate are compared with existing such as Virtual Interactive Animation Design with Restricted Boltzmann machine (VI-AD-RBM),Generative deep learning for visual animation in landscapes design using Generative adversarial network(GDL-VALD-GAN), and Hand interface using deep learning in immersive virtual reality using convolutional neural network(HID-LVIR-CNN)respectively.