Control Model Masking for Multi-Condition Image Synthesis

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Solution Overview

Problem

Existing image synthesis methods using generative AI face challenges in maintaining image quality when combining multiple conditions such as Canny edge and semantic label maps, leading to degradation in image synthesis, particularly in complex environments like autonomous driving scenarios.

Innovation Solution

A method involving a control model like ControlNet that allows for the selection and combination of multiple conditions, with spatially defined masks to exclude certain areas from condition influence, enabling flexible and precise image generation by training the model to handle combined conditions end-to-end.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If multiple conditions (Canny edge, depth map, semantic label map) are combined for image synthesis, then control precision and detail accuracy are improved, but image synthesis quality degrades

Engineering Contradiction:

Improvecontrol precisionVSAvoidimage synthesis quality

Core Design Contradiction:

Measurement precisionVSManufacturing precision

Solution Approach 1:

The image canvas is divided into multiple spatial regions, each assigned different condition masks. This segmentation allows different areas to have different levels of condition application, preventing overwhelming the generative model with too many conditions in a single region while maintaining precise control where needed.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Different spatial regions are assigned different condition masks (e.g., first condition mask for first region, second condition mask for second region). This local differentiation enables precise control in specific areas while maintaining image synthesis quality in other regions, resolving the contradiction between control precision and overall image quality.

Inventive Principle:

Principle #3Local quality

2Adaptability or versatility

If multiple conditions are applied across the entire image, then comprehensive control is achieved, but the generative model's creative freedom and image quality are reduced

Engineering Contradiction:

Improvecontrol flexibilityVSAvoidimage synthesis quality

Core Design Contradiction:

Adaptability or versatilityVSManufacturing precision

Solution Approach 1:

The image is segmented into multiple regions with different condition applications. This allows the system to maintain comprehensive control flexibility across the entire image while preserving quality by limiting condition density in any single region.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Instead of applying all conditions uniformly across the entire image, the invention applies conditions partially to specific regions only. This partial action approach maintains the versatility of having multiple conditions available while avoiding the quality degradation that would result from applying all conditions everywhere.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20260051085A1Method for generating a dataset for training and/or testing a machine learning system

Publication Date: 2026.02.19 ROBERT BOSCH GMBH

AI summary

The invention relates to a method (100) for generating at least one data set for training and/or testing a machine learning system (55), the generation being provided by a control model (50).