Built Environment Image Visualization With Segment-Level Refinement
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Solution Overview
Problem
Conventional generative artificial intelligence pipelines struggle with providing users the ability to control fine details of image transformations and make precise modifications to discrete portions of images, making it difficult to implement multiple changes or revert specific alterations.
Innovation Solution
A system and method that enables the transformation of an original image based on a stylistic prompt, followed by controlled-segment level refinement using machine-learning models to identify and replace architectural features in the image, allowing users to interactively modify and enhance specific segments of the transformed image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a conventional generative artificial intelligence pipeline is used to transform an original image, then the overall stylistic transformation can be achieved, but the user cannot control fine details or make precise modifications to discrete portions of the image
Solution Approach 1:
The transformed image is segmented into multiple discrete portions or regions, allowing users to select and modify specific segments independently. The system divides the image into controllable units (e.g., furniture items, architectural features) that can be individually targeted for refinement or modification while preserving the overall stylistic transformation.
Solution Approach 2:
The system applies different levels of control and processing to different regions of the image. Users can apply stylistic transformations globally while maintaining the ability to make precise local modifications to specific segments. This allows fine detail control in selected areas without affecting the entire image uniformly.
2Adaptability or versatility
If a conventional generative artificial intelligence pipeline is used, then image transformation can be performed, but users cannot make multiple changes or revert specific alterations
Solution Approach 1:
The system maintains a dynamic state where users can iteratively modify different segments of the image multiple times. Each segment can be independently modified, reverted, or adjusted without affecting other segments. The system allows users to navigate through multiple versions and make sequential changes to achieve the desired final composition.
Solution Approach 2:
The system preserves copies of original segments and previous versions, allowing users to revert to earlier states or compare different modification options. This enables flexible experimentation where users can try multiple design iterations and selectively accept or reject changes.
3Manufacturing precision
If machine-learning models are used to identify and replace architectural features, then precise segment-level refinement is achieved, but the system complexity increases
Solution Approach 1:
Machine-learning models serve as intermediary components that automatically perform complex tasks such as image segmentation, feature identification, and boundary detection. These intermediaries bridge the gap between user intent and precise image modification, handling the computational complexity while providing a simplified user interface for control.
Solution Approach 2:
The system employs automated machine-learning models that perform segmentation and feature identification without requiring manual user input for each segment. The models autonomously analyze the image, identify architectural features, and prepare segments for modification, reducing the operational burden on users while maintaining high precision.
Data Source
AI summary
Systems and methods are disclosed for generating an image of a built environment using a visualization application and an application system. The application system can obtain from the visualization application an indication of an original image and of a style. The application system can detect a characteristic of the original image and enrich a style conditioning prompt based on the detected characteristic. The application system can obtain a transformed image generated using the original image and the style conditioning prompt. The application system can provide the transformed image or an annotated version of the transformed image to the visualization application for display. The application system can receive from the visualization application instructions to generate an updated version of the transformed image. The instructions can include selection of a segment of the transformed image. The application system can generate and provide to the visualization application for display an updated transformed image.