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UniPart, a feed-forward cross-modal 3D Transformer that conditions CLIP text embedding, is introduced, a feed-forward cross-modal 3D Transformer that achieves strong zero-shot results on open-vocabulary part benchmarks and transfers to language-conditioned part grasping in real world.
DA-Nav is proposed, a Direction-Aware vision-language Navigation framework that reformulates navigation as a discrete spatial grounding problem on the egocentric 2D image plane, outperforming existing State-of-The-Art (SoTA) methods while maintaining a substantially stronger recovery capability.
This work introduces HumanTracker, a preference-aligned metric trained on 12K motion pairs containing 24K motions that better predicts human preferences and reveals contact and stability failures that kinematic metrics often miss.
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