Break-A-Scene: Extracting Multiple Concepts from a Single Image

Abstract:

"Text-to-image model personalization aims to introduce a user-provided concept to the model, allowing its synthesis in diverse contexts. However, current methods primarily focus on the case of learning a single concept from multiple images with variations in backgrounds and poses, and struggle when adapted to a different scenario. In this work, we introduce the task of textual scene decomposition: given a single image of a scene that may contain several concepts, we aim to extract a distinct text token …

abstract case computervision concept diverse focus image images multiple personalization synthesis text text-to-image work