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Training with just 3% of high-quality motion data can yield superior tracking performance compared to using the entire dataset.
Humanoid-GPT achieves unprecedented zero-shot generalization in motion tracking, outperforming traditional models by leveraging a billion-scale motion dataset.
MLLMs still can't handle time-sensitive multimodal reasoning, often failing to integrate auditory and visual cues effectively in dynamic environments like a 4D escape room.
