Real-Time Interaction in AR/VR Environments: A Deep Learning Approach to Human-Computer Interaction

AR and VR technologies represent Innovative technologies of HCI providing rich and dynamic user experiences. This work proposes a framework based on deep learning for interacting in real-time with AR/VR environments and employing innovative multimodal input handling and flexible response strategies. The implementation consists of convolutional neural networks (CNNs) for gesture recognition, recurrent neural models for natural language understanding, and transformer-based models for gaze tracking. These models are latency-reduced and are deployed with minimal overhead architectures to fit into constrained AR/VR devices. Several assessments are done based on actual data, and mock AR/VR is used to help prototypes. Experimental evaluation confirms that the proposed framework has better interaction latency, accuracy, and adaptability over conventional approaches. For instance, gesture recognition has a success rate of 96.3% the response is facilitated in less than 50 and the natural language commands have a processing delay of 30 ms. The user surveys support the case that the system effectively presents context-based and individualized communication making the system more engaging and easier to use. It focuses on the issues that arise while implementing such methods, including computational load and integration difficulties, and provides a more detailed version of this work that describes solutions to them. The findings map the feasibility of deep learning in augmenting HCI within the AR/VR systems, which will help propel advancements across education, healthcare, and more notably, game-designing and telepresence.