Sketch-to-Scene Generative Drawing with Depth-Aware Outpainting
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Solution Overview
Problem
Existing text-to-image models lack the capability to generate diverse and detailed generative drawings that meet design specifications, leading to inefficiencies and frustration in design cycles, particularly for complex products like vehicles, as they require manual manipulation and additional iterations.
Innovation Solution
An illustration system that uses a learning model to generate drawings from sketches and text, predicts realistic prompts and scaling amounts, and estimates depth maps to render drawings within realistic scenes, incorporating iterative sketching and feedback loops to enhance design efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a text-to-image model is used to generate designs rapidly, then productivity is improved, but manufacturing precision deteriorates due to lack of design specification compliance
Solution Approach 1:
The system implements feedback loops where design specifications are continuously evaluated against generated images, and the generation parameters are adjusted based on compliance assessment results to maintain both speed and precision
Solution Approach 2:
An intermediary evaluation module is introduced between the text-to-image model and final output, which assesses design specification compliance and guides iterative refinements without significantly increasing overall processing time
2Manufacturing precision
If manual manipulation is performed to meet design specifications, then manufacturing precision is improved, but productivity deteriorates due to increased design time
Solution Approach 1:
The system enables self-service automation where the design generation system automatically adjusts and refines outputs to meet specifications without requiring manual designer intervention, maintaining precision while preserving productivity
3Device complexity
If the text-to-image model searches within a discrete scope, then device complexity is reduced, but adaptability deteriorates due to limited design diversity
Solution Approach 1:
The system expands the search space by introducing additional dimensions such as multiple style domains and parameter spaces, allowing the model to explore diverse design possibilities while maintaining manageable complexity through structured exploration
4Device complexity
If generative drawings are created without depth estimation, then device complexity is reduced, but manufacturing precision deteriorates due to lack of realistic scene rendering
Solution Approach 1:
The system segments the image generation process into distinct modules, with depth estimation as a separate optional component that can be added to enhance realism without fundamentally complicating the core generation architecture
Data Source
AI summary
Systems, methods, and other embodiments described herein relate to creating a realistic scene including a generative drawing using a learning model and image manipulation. In one embodiment, a method includes generating a drawing from a drawn line and text by a machine learning (ML) model. The model also includes predicting a realistic prompt and a scaling amount about the drawing using a language model and estimating a depth map of the drawing using a depth model according to the scaling amount. The model also includes rendering the drawing within a realistic scene by an outpainting model using the realistic prompt and the depth map.