Intel Labs

Enhancing Photorealism Enhancement

Advancing the photorealism of synthetic imagery requires a sophisticated deep learning framework that bridges the gap between rendered data and real-world visual fidelity.

• A new rendering-aware denormalization network improves synthetic-to-real image translation by integrating geometry, material, and lighting buffers to ensure high-fidelity visual output.
• The framework utilizes a multi-discriminator training protocol and patch-matching sampling to prevent GAN-related hallucinations and maintain semantic consistency between synthetic and real datasets.
• The semantically aligned Kernel VGG Distance metric provides a more accurate assessment of perceptual realism than traditional metrics like the Kernel Inception Distance, which prioritize semantic structure over visual quality.