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This work introduces a motion- and gravity-aware framework under the Learning Using Privileged Information (LUPI) paradigm, and suggests that LUPI offers a practical way to inject motion and physical priors into feed-forward 3D reconstruction without increasing test-time complexity.
Space Tokens is introduced, a lightweight, architecture-agnostic framework that equips VLMs with explicit continuous spatial representations without requiring additional inference-time modules, and demonstrates that continuous spatial tokens provide an effective, interpretable, and computationally efficient mechanism f...
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