Search papers, labs, and topics across Lattice.
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The challenge revealed that innovative techniques can significantly enhance visibility for autonomous vehicles in rainy conditions, a key hurdle for safe navigation.
DreamHand achieves a groundbreaking 40% reduction in error for 3D hand trajectory recovery in occluded environments, setting a new standard for egocentric video analysis.
Annotations can be transformed into oracle rollouts, drastically improving the efficiency and scalability of reinforcement learning for video MLLMs.
PhysMani achieves unprecedented success rates in dynamic object manipulation by accurately predicting future 3D scene dynamics through a physics-informed approach.
DrivingDepth achieves state-of-the-art depth estimation by leveraging sparse LiDAR to fine-tune pixel-wise scale without sacrificing geometric coherence.
Transforming passive materials databases into an active Materials Bank could revolutionize how we identify and leverage high-potential materials for industrial innovation.
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.
