Neural Network Image Generation via Semantic Maps

The patent replaces the traditional mechanical graphics rendering system (which explicitly simulates geometry, materials, and light transport) with a data-driven neural network model. The neural network learns photo-realistic image generation from training data, substituting complex physical simulations with learned patterns, thereby reducing rendering complexity while maintaining image quality.

The patent transforms the rendering approach by changing the fundamental parameters from explicit geometric and material definitions to learned representations from training data. The neural network models learn to generate images by capturing statistical patterns and relationships from training examples, fundamentally altering how photo-realism is achieved from physics-based simulation to data-driven generation.

The patent applies preliminary action by pre-training neural network models on extensive datasets before actual rendering. The model learns from training data in advance, so that during actual use, photo-realistic images can be generated quickly without performing complex simulations in real-time. This preliminary learning phase transfers computational burden from runtime to training time.

The patent substitutes the time-consuming explicit simulation process with a pre-trained neural network that can generate photo-realistic images rapidly. The neural network replaces the sequential, computationally intensive graphics rendering pipeline with a learned model that produces results much faster, significantly reducing time consumption while maintaining image quality.