UniReal - A universal image generation and editing framework jointly developed by the University of Hong Kong and Adobe.

What is UniReal?

UniReal is a framework jointly developed by the University of Hong Kong and Adobe Research, focusing on enabling various image generation and editing tasks. Based on simulating real-world dynamics, the framework can handle a wide range of tasks, including image generation, editing, customization, and compositing, within a single model. UniReal treats varying numbers of input and output images as video frames, using large-scale video data as a general source of supervision to learn consistency and variability, generating realistic images. UniReal demonstrates exceptional capabilities in handling complex scenes such as shadows, reflections, lighting effects, and object pose changes, and can be extended to new application areas.

UniReal's main functions

• Image generationGenerate new image content based on text prompts.
• Image editingIt supports editing existing images, such as adding, removing, or replacing objects in the image.
• Image customizationUsers can customize images to meet specific visual elements or style requirements.
• Image synthesisCombine elements from multiple images into a new image.
• Style conversionFrames can change the style of an image, such as converting it to a watercolor style.
• Depth estimation and image understandingUniReal can predict the depth map of an image for image understanding and analysis.

UniReal's technical principles

• Video generation frameworkBased on the design principles of video generation models, the image task is regarded as a problem of generating "discontinuous" video frames.
• Full attention modelThe framework uses a full attention mechanism to model the relationships between frames and process input and output images.
• Hierarchical promptsUniReal employs a hierarchical cues scheme, including basic cues, contextual cues, and image cues, to reduce ambiguity during training and inference.
• Text-image association: Constructing embedding pairs associates visual tags with corresponding text, allowing the model to reference specific images based on text prompts.
• Data building: Construct training data from video data to support a variety of image generation and editing tasks with natural consistency and variability between video frames.
• General supervisionThe framework uses large-scale video data as a general source of supervision to learn how to capture visual changes while maintaining consistency across different images.

UniReal's project address

• Project official website:xavierchen34.github.io/UniReal
• arXiv technical paper:https://arxiv.org/pdf/2412.07774

UniReal Application Scenarios

• Digital content creationArtists and designers generate or edit images to create new artworks or design concept sketches.
• Media and EntertainmentIn film and game production, it enables rapid prototyping and concept validation, generating realistic backgrounds and scenes.
• Advertising and MarketingMarketers can customize advertising images to quickly respond to market changes and customer needs.
• e-commerceE-commerce platforms offer virtual try-on services, showcasing how clothing looks on different models.
• Education and trainingIn the field of education, creating realistic teaching materials and simulated scenarios enhances the learning experience.