Chao Ma

PLAT bridges dense motion tracking and sparse goal-conditioned control by exploiting dense goal sequences as privileged supervision during training while requiring only sparse timed keyframe commands at deployment.

MEgoVista is offered as a measured route from egocentric video to metric hand supervision, one that widens where such labels can be gathered, one that widens where such labels can be gathered.

Tracking both task advantage and actual reward gains can drastically improve the efficiency of multi-task reinforcement learning for LLMs.

SparseOcc++ achieves a 2.3-point IoU improvement while being 3.9 times faster than previous methods, revolutionizing 3D semantic occupancy prediction.

Targeted structural completion can slash Gaussian usage by 74% and rendering time by 34%, revolutionizing 3D reconstruction in autonomous driving.

PixelPilot redefines trajectory prediction in autonomous driving by transforming it into scalable 2D tasks, leading to unprecedented generalization across heterogeneous datasets.