Immersive remote operation relies on realistic and consistent scene reconstruction from limited input data. Traditional view synthesis methods often struggle with inconsistencies, artifacts, and computational inefficiencies, making real-time applications challenging. This project aims to develop an advanced framework for efficient and high-quality view synthesis tailored for immersive remote operation. The focus will be on ensuring temporal and spatial consistency, improving real-time performance, and adapting to dynamic environments. The proposed approach will integrate novel techniques to enhance rendering quality while maintaining computational efficiency. To effectively train and test large-scale models, access to high-performance GPUs with significant memory and compute power is essential. Specifically, GPUs like the NVIDIA A100, with high memory capacity and extensive CUDA cores, will be required to handle the computational demands of deep learning-based view synthesis. To achieve these goals, high-performance computing resources will be required for training and evaluation. The outcomes of this project will contribute to real-time immersive systems with potential applications in telepresence, robotics, and remote collaboration.