PUG (Photorealistic Unreal Graphics)
Meta's PUG uses Unreal Engine to generate photorealistic, semantically controllable synthetic datasets for vision research.
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Unreal-powered synthesis: Leverages Unreal Engine's photorealistic rendering to produce high-fidelity synthetic training images with precise semantic control.
Controllable semantics: Researchers can specify scene content, lighting, camera angles, and object configurations, making targeted ablations possible.
Democratizing synthetic data: Lowers the barrier to photorealistic synthetic data generation, previously limited to groups with custom rendering pipelines.
Rigorous evaluation: Enables more rigorous evaluations of vision-model robustness to controlled distribution shifts - lighting, occlusion, pose - than natural data allows.