Generating high-quality custom videos remains a significant challenge, because video generation models are limited to their pre-trained knowledge. This limitation affects industries such as advertising, media production, education, and gaming, where customization and control of video generation is essential.
To address this, we developed a Video Retrieval Augmented Generation (VRAG) multimodal pipeline that transforms structured text into bespoke videos using a library of images as reference. Using Amazon Bedrock, Amazon Nova Reel, the Amazon OpenSearch Service vector engine, and Amazon Simple Storage Service (Amazon S3), the solution seamlessly integrates image retrieval, prompt-based video generation, and batch processing into a single automated workflow. Users provide an object of interest, and the solution retrieves the most relevant image from an indexed dataset. They then define an action prompt (for example, “Camera rotates clockwise”), which is combined…
