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Reducing inter-view redundancy, PointSplat achieves superior 3D human representation quality while minimizing computational load.
Overcoming perceptual uncertainty in vision-language navigation is now possible by explicitly modeling geometric, semantic, and appearance uncertainty with a novel Uncertainty-Aware Gaussian Map.
Synthesizing realistic duet dance motions gets a boost from explicitly modeling inter-dancer contact, leading to significantly improved interaction fidelity and rhythmic synchronization.
Photorealistic simulation with Gaussian Splatting and drivable avatars closes the reality gap, enabling embodied agents to learn human-aware navigation policies that generalize better to the real world.
Achieve state-of-the-art 3D reconstruction in large-scale scenes by rapidly adapting lightweight neural networks during test time to capture global context.
