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PhysMani achieves unprecedented success rates in dynamic object manipulation by accurately predicting future 3D scene dynamics through a physics-informed approach.
Transforming passive materials databases into an active Materials Bank could revolutionize how we identify and leverage high-potential materials for industrial innovation.
DrivingDepth achieves state-of-the-art depth estimation by leveraging sparse LiDAR to fine-tune pixel-wise scale without sacrificing geometric coherence.
Achieving trillion-parameter performance with just 35 billion parameters by scaling agent horizons reveals a new frontier in model efficiency.
Robots can now plan 9x faster and achieve significantly higher success rates by decoupling action prediction from video generation in World-Action Models.
