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LLMs, like humans, exhibit a "frequency bias," performing better when prompted and fine-tuned with more common textual expressions.
Training generative inverse renderers on AAA game footage closes the realism gap, enabling superior generalization and controllable generation compared to models trained on existing synthetic datasets.
