Towards Automatic Content Generation for Immersive Cinema Theater Based on Artificial Intelligence
David Traparic, Mohamed–Chaker Larabi, Ladjel Bellatreche · 2023
Immersive display systems like the one proposed by ICE® technology aims to enhance visual immersion by widening the field of view. However, creating immersive content while maintaining immersion integrity is a challenging task due to the sensitivity of human peripheral vision to flickering and movement. Moreover, identifying elements in videos that may disrupt immersion and determining whether they can be expanded into an immersive context is a complex and time-consuming process due to the lack of automatic methodologies. In this paper, we propose a pipeline for automatically generating content for lateral displays from movies. The pipeline consists of several steps. Firstly, the input content is divided into cinematic shots, and then further segmented into snippets. Next, domain-specific features are extracted using dedicated video deep learning models. Additionally, handcrafted features are computed to provide task-specific information. These extracted features are utilized to predict the required processing steps for generating lateral content that aligns with ground-truth annotations provided by cinema experts. The results obtained from our pipeline show promising accuracy and demonstrate the potential for this specialized application.