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A single unified model can outperform specialized systems across various computer vision tasks, all without the need for custom architectures.
Achieve 9x lower trajectory error and 3x better FID in motion generation by using a diffusion-based discrete motion tokenizer that elegantly handles both semantic and kinematic constraints.
