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National Institute of Health Data Science, Peking University
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Atomic movements enable a new level of control and coherence in dance generation, transforming how machines interpret and produce choreography.
I2V models not only excel at dynamic editing but also provide a unique lens for diagnosing errors in Human-Object Interaction tasks.
Counterintuitively, letting radar cardiac sensors learn to mimic ECGs first yields far better performance on downstream tasks like blood pressure regression and waveform segmentation than directly training on those tasks.
Reconstructing high-fidelity 3D heart models from noisy radar data is now possible, thanks to a novel mesh deformation approach that leverages physics-informed learning.
