Effective science communication, particularly in astronomy, remains a significant challenge for research institutions due to the resource-intensive nature of video production. Standard dissemination cycles typically demand substantial time and personnel. To address this, an automated system was developed to transform digital content into monthly astronomical summary videos, allowing users to define content descriptions and visual assets for automated processing.
      The system integrates core functionalities such as text-to-animation conversion, image composition, and automated publishing workflows. Its technical framework leverages the N8N platform for workflow automation, web servers for content management, and AI-driven image processing. These components work together to streamline the transition from raw digital materials to finalized multimedia outputs.
     The researchers implemented this automated workflow to eliminate redundant tasks and enhance production efficiency for astronomical media. Performance benchmarking revealed distinct advantages among AI models: Runway (Model A) excelled in processing speed, averaging 2 minutes at a 1200×780 resolution, while Framepack (Model C) offered moderate resolution at 770×520. Additionally, the F5-TTS-Thai speech synthesis and image models operate as local-based, cost-free solutions. These findings confirm that selecting the optimal model configuration effectively meets institutional requirements while optimizing operational resources.