Should I Render or Should AI Generate? Crafting Synthetic Semantic Segmentation Datasets With Controlled Generation

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Generating labeled synthetic data for computer vision tasks, such as semantic segmentation, is crucial but challenging.
• Traditional methods using computer graphics simulations and rendering often fail to capture real-world complexities like varied weather conditions and fine details.
• These methods are also costly and time-consuming, limiting the diversity and scale of available datasets.

Purpose of the Study:

• To explore the integration of generative AI, specifically controllable diffusion models, for automatic synthetic image-labeled data generation.
• To introduce and test a novel methodology for creating labeled synthetic images, focusing initially on semantic segmentation.
• To compare the effectiveness of AI-generated data against traditional rendering techniques in dataset creation and model training.

Main Methods:

• Leveraged controllable diffusion models to generate synthetic image variations guided by text prompts and semantic masks.
• Developed a novel methodology for generating labeled synthetic images suitable for semantic segmentation tasks.
• Tested the approach in two distinct image segmentation domains.

Main Results:

• The proposed AI-driven approach efficiently created diverse datasets compared to traditional computer graphics simulations.
• Downstream models trained on AI-generated data demonstrated superior performance.
• The results indicate a paradigm shift towards controlled generation models for synthetic data creation.

Conclusions:

• Controllable diffusion models offer a powerful and efficient alternative to traditional rendering for generating high-quality, labeled synthetic image data.
• AI-generated datasets can effectively train computer vision models, outperforming those trained on traditionally rendered data.
• The study advocates for the adoption of generative AI in synthetic data creation, questioning the necessity of traditional rendering ('Should I render or should AI generate?').