Controlled Image Generation With Masked Composite Blending
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Solution Overview
Problem
Current image generation tools using machine learning models require careful prompt engineering and lack fine-grained control over the integration of new visual elements, making it difficult to seamlessly incorporate new objects, textures, or styles into existing images while maintaining visual coherence and plausibility.
Innovation Solution
A controlled image generating ML model is used, guided by content and appearance images, with a mask image controlling the area of integration, allowing for precise control over the incorporation of new visual elements into a scene image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional image editing techniques are used, then visual coherence and plausibility can be maintained, but the process becomes complex and labour-intensive
Solution Approach 1:
The patent replaces manual mechanical image editing operations with an automated machine learning system. The controlled image generating model automatically performs masking, blending, and composition tasks that traditionally required manual intervention, thereby maintaining visual coherence while eliminating labour-intensive processes
Solution Approach 2:
The patent introduces control images as intermediary elements that mediate between the user's intent and the final composite image. These control images provide fine-grained guidance to the ML model, enabling precise control over visual element integration without requiring manual editing of each element
2Extent of automation
If current ML-based image generation tools are used, then automation is improved, but fine-grained control over visual element integration is lost
Solution Approach 1:
The patent segments the control process into multiple independent control images, each responsible for specific aspects of the composite image generation. This segmentation allows users to control different visual elements (masking, lighting, composition) separately while maintaining automated processing, thereby achieving both fine-grained control and automation
Solution Approach 2:
The patent adds a new dimension of control by introducing control images as a separate layer of input. Instead of controlling the ML model through single text prompts, users can provide multiple control images that operate in an additional dimensional space, enabling precise control over various aspects of image generation simultaneously
3Reliability
If manual masking and blending techniques are used, then visual plausibility is maintained, but productivity decreases
Solution Approach 1:
The patent replaces manual masking and blending operations with automated ML-based operations. The controlled image generating model automatically performs these tasks by learning from control images, maintaining visual plausibility through intelligent processing while dramatically increasing productivity by eliminating manual intervention
Solution Approach 2:
The patent enables the ML model to self-adjust and self-optimize the blending and masking operations by learning from control images. The model automatically adapts to maintain visual plausibility without requiring manual tuning or intervention, thereby maintaining quality while improving productivity
Data Source
AI summary
Computer implemented methods and associated systems are described, which have particular application to image generation by machine learning models. A method of generating a composite image is described that is based on two images using a controlled machine learning model. A method of processing a composite image is also described which includes determining that a transition region of the composite image is similar to one of the images on which the composite image was based and using in the transition region visual elements from the basic image. A method for providing a user interface is also described. The method includes displaying representations of images generated using common input and different hyperparameters.