Controllable synthetic image generation

Controllable synthetic image generation data is a crucial component in training deep learning models, especially for computer vision tasks, where models rely on large, diverse, and well-annotated datasets to learn visual patterns and make accurate predictions. However, collecting and labeling real-world data is often expensive, time-consuming, and difficult to scale. Annotation requires significant human effort and expertise, particularly when precise labels like segmentation masks or bounding boxes are needed. Moreover, capturing edge cases—such as rare scenarios or dangerous situations like traffic accidents or hazardous environments—is particularly challenging, either due to their infrequency or ethical and safety concerns during data collection. In this context, frameworks that enable synthetic image generation become highly valuable. They allow for the creation of diverse and annotated datasets at scale, including rare or risky scenarios, helping overcome many limitations of traditional data collection and significantly accelerating the development of robust computer vision models.

We have developed a novel framework for synthetic image generation that focuses on modifying existing images rather than creating them entirely from scratch. Our approach enables precise control over which regions of an image are altered and which remain unchanged. Users can either select regions to modify or mark areas that must be preserved, and then provide a prompt describing the desired transformation.

Key Advantages

This region-based methodology offers several advantages, particularly in data augmentation and content consistency:

Annotation Preservation

By controlling which parts of the image are modified, we ensure that existing annotations (e.g., bounding boxes, masks) remain valid post-generation. This significantly reduces the need for manual re-annotation, a common bottleneck in training data pipelines.

Unlimited Data Augmentation

Leveraging existing datasets, we can generate an effectively unlimited number of variations, enriching training sets with greater diversity and edge cases.

Targeted Edge Case Generation

We can easily produce rare or difficult scenarios (e.g., extreme weather conditions, lighting variations) by modifying only relevant regions of the image, while keeping the rest intact. Our data generation framework is essential in accelerating the development of machine learning models for our clients. It enables faster iteration cycles, reduces overall development costs, and contributes to improved model performance and quality. Our data generation framework not only enhances model training through synthetic data creation but also serves as a powerful standalone solution. For example, in the fashion industry, it can be used to generate high-quality pack shot sessions, eliminating the need for costly and time-consuming photoshoots. Please read more in our solutions sections.
Solutions