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Synthetic data can significantly boost controllable human video generation, but only if you carefully select which synthetic samples to use.
Generating realistic human interactions with objects and scenes is now possible even with limited data, thanks to a clever training strategy that combines synthetic and real data.
LLMs can learn to generate high-quality symbolic world models by interacting with a multi-agent system that provides adaptive, behavior-aware feedback, closing the gap between static validation and interactive execution.
