Deep Dream-Guided Neural Style Transfer for Real-Time Artistic Effects in Augmented Reality Systems
Abstract
Augmented Reality (AR) systems are increasingly integrating artistic visual effects to enhance user experience, with Neural Style Transfer (NST) offering a powerful means of transforming visual content. However, existing NST methods often struggle with maintaining real-time performance and stylistic richness on resource-constrained AR platforms. Current approaches usually lack temporal coherence and produce either overly smoothed or computationally intensive outputs, rendering them unsuitable for dynamic, real-time AR applications. To address these challenges, this study proposes a novel framework, Deep Dream-Guided Neural Style Transfer (DD-GNST), which integrates Deep Dream's feature amplification capabilities with efficient Neural Style Transfer (NST) for enhanced artistic expression. The proposed DDGNST framework employs a lightweight encoder-decoder structure, where Deep Dream layers guide stylization by amplifying specific neural activations, yielding more vivid and surreal artistic effects. This enables the system to generate temporally stable, visually appealing output in real-time, suitable for AR headsets and mobile devices. Experimental results demonstrate that DD-GNST outperforms conventional NST models in both processing speed and visual quality, maintaining temporal consistency across frames. This approach paves the way for immersive, artistic AR experiences in live video streaming, gaming, and creative mobile applications. The proposed method achieves a frame rate increase of 98.4%, a temporal consistency score of 97.8%, a stylization quality of 96.3%, and a model size reduction of 98.5%.