With the continuous advancement of technology, the application of generative artificial intelligence (AI) in various fields is gradually demonstrating great potential, particularly when combined with Extended Reality (XR), creating unprecedented possibilities. This survey article systematically reviews the applications of generative AI in XR, covering as much relevant literature as possible from 2023 to 2025. The application areas of generative AI in XR and its key technology implementations are summarised through PRISMA screening and analysis of the final 26 articles. The survey highlights existing articles from the last three years related to how XR utilises generative AI, providing insights into current trends and research gaps. We also explore potential opportunities for future research to further empower XR through generative AI, providing guidance and information for future generative XR research.

The development of Internet of Things (IoT) systems has enabled disaster perception and prediction to be highly accurate. On this basis, high-quality post-disaster Point of Interest (POI) visit data can help city decision-makers develop more sophisticated recovery plans to minimize the cost of recovery. This work focuses on the problem of POI visits generation in post-disaster recovery scenarios, utilizing diffusion model to generate visit recovery curves base on the data from sensor networks. We take the disaster severity as condition and propose a disaster mapping method to map the sensor data to each POI.

Steady-State Evoked Potential (SSVEP) is a brain response to specific frequency visual stimuli, used in brain-computer interfaces due to its robust and easily detectable signals. Researchers have long applied methods like Canonical Correlation Analysis and deep learning for SSVEP signal decomposition and classification. However, those methods struggle to classify SSVEP signals without new subject’s data, and calibration is time-consuming. In this paper, we propose a two-stage, two-Transformer streams network to address the challenge of classifying SSVEP signals from new subjects. We utilize hierarchical contrastive learning to project features into a more discriminable feature space before classification. The comparative experiment demonstrates that our approach exhibits superior performance relative to alternative methods in processing SSVEP signals from new subjects.

Accurate motion capture of aerial robots in 3D is a key enabler for autonomous operation. Recently, some research considers using MM-Wave radar sensors for drone motion capture. However, due to the high noise and difficulty in capturing the center of an object in MM-Wave radar, the existing traditional methods have achieved unsatisfactory results. We develop a novel adaptive chirps domain window order method for MM-Wave radar data and customize a neural network architecture.