What is Generative AI and Style Transfer?

Generative AI involves using machine learning models to create new data that mimics the properties of real-world data. Style transfer is a specific application of generative AI where the characteristics of one image (such as camera and scenery attributes) are applied to another image, enhancing its realism.

Request Data Generation

Substrate with additive manufactured welding seam.

Example showing details and realism added with generative AI, to represent welding seams in an additive manufacturing process.

The Importance of Style Transfer in Synthetic Image Data

When generating synthetic image data, incorporating the specific visual characteristics of real images, such as sensor noise patterns, contrast, and environmental features (e.g., dirt, blur), is crucial for creating realistic datasets. Without these details, synthetic images can appear artificial and may not be effective for training machine vision models.

How We Use Generative AI for Style Transfer

Our approach combines structured domain randomization and 3D rendering with generative AI techniques to apply pixel-level details from real images to synthetic ones. This process involves:

Analysing Real Data
‍Identifying and extracting key visual features from real images, such as camera sensor characteristics and environmental details.
Applying Features to Synthetic Data
‍Using generative AI models to transfer these features onto synthetic images, enhancing their realism and making them more representative of real-world conditions.

Benefits of Generative AI for Style Transfer

Enhanced Realism
Adds crucial details that make synthetic images indistinguishable from real ones, improving the effectiveness of training datasets.
Consistency
Ensures that the added details are applied uniformly across the dataset, maintaining high quality and accuracy.
Efficiency
Reduces the need for extensive manual adjustments, streamlining the data generation process.

Additional Resources

Explore Key Concepts and Benefits of Synthetic Data and corresponding Annotations.

post_add

Why Use Synthetic Data?

Understand the benefits of synthetic data, including enhanced model training, cost efficiency, and the ability to generate rare or hard-to-capture scenarios.

Learn more

trending_up

How Real is Synthetic Data?

Explore the realism and accuracy of synthetic data compared to real-world data, and how it can be tailored to match specific use cases.

Learn more

label

Annotations

Explore the critical role of high-quality annotations in dataset preparation. Our synthetic image data comes fully annotated, as our generation process precisely tracks and identifies every element within each image, ensuring consistent and accurate labelling.

Learn more

view_in_ar

3D Rendering

Our 3D rendering process leverages advanced computer graphics techniques to create highly realistic and detailed synthetic images. This approach allows us to simulate a wide range of scenarios and environments.

Learn more

hub

Generative Approaches

Our generative data creation techniques use advanced AI models to enhance realism and add details to the 3D rendered synthetic image.

Learn more