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Real-world tennis serving by humanoid robots is now possible without motion capture, thanks to a novel adaptive framework that learns directly from video.
HiPHI significantly expands the diversity of human motion data, offering a game-changing resource for training humanoid AI in complex real-world tasks.
Target frame reconstruction from sparse event data achieves up to 3.29 dB improvement in PSNR, showcasing a breakthrough in video fidelity.
A new flow matching prior can drastically enhance humanoid motion tracking by guiding policy exploration with geometric insights from unordered pose data.
