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By separating known and unknown object representations into orthogonal subspaces, DEUS achieves state-of-the-art open world object detection, outperforming prior methods that struggle to learn distinct unknown object representations.
By treating 3D scene editing as goal-regressive planning rather than pure generation, Edit-As-Act achieves instruction fidelity, semantic consistency, and physical plausibility that existing methods miss.
