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Heterogeneous diffusion models can be seamlessly stitched together, unlocking new capabilities by sharing a common latent language.
The results suggest that effective transfer from 3D reconstruction pre-training to 3D tracking is not merely a matter of partial fine-tuning, but depends on a tracking-oriented transfer principle that preserves shallow geometry while adapting deeper representations to the downstream objective.