arXiv subjects

Saverio Iacono

Publications and source records attributed to Saverio Iacono.

2 recordsLinked to original sources

arXiv2026

SAMPAI: A VR Framework for Industrial Safety Training Inspired by Cultural Heritage Education

This study explores the application of Virtual Reality (VR) to industrial safety training by adapting immersive design principles from cultural heritage education. The SAMPAI simulator, developed for the IPLOM refinery in Busalla (Italy), offers a controlled environment that enhances spatial awareness and procedural retention in high-risk scenarios. The project emphasizes user-centered interaction and anticipates future integration with Augmented Reality (AR) for real-time operational support. To ensure reliability, a rigorous validation protocol is proposed: initial testing with a small operator group will identify usability issues, followed by incremental expansion to strengthen system robustness and real-world relevance. By shifting training from abstract instruction to situated, experiential learning, SAMPAI bridges humanistic and technical domains. The result is a scalable, immersive framework that enhances readiness and supports safe, informed decision-making in complex industrial environments.

cs.HC↗

arXiv2025

Multi-Stage Generative Upscaler: Reconstructing Football Broadcast Images via Diffusion Models

The reconstruction of low-resolution football broadcast images presents a significant challenge in sports broadcasting, where detailed visuals are essential for analysis and audience engagement. This study introduces a multi-stage generative upscaling framework leveraging Diffusion Models to enhance degraded images, transforming inputs as small as $64 \times 64$ pixels into high-fidelity $1024 \times 1024$ outputs. By integrating an image-to-image pipeline, ControlNet conditioning, and LoRA fine-tuning, our approach surpasses traditional upscaling methods in restoring intricate textures and domain-specific elements such as player details and jersey logos. The custom LoRA is trained on a custom football dataset, ensuring adaptability to sports broadcast needs. Experimental results demonstrate substantial improvements over conventional models, with ControlNet refining fine details and LoRA enhancing task-specific elements. These findings highlight the potential of diffusion-based image reconstruction in sports media, paving the way for future applications in automated video enhancement and real-time sports analytics.

cs.CV↗