A causal transformer trained on synthetic seismograms autoregressively continues three-component waveforms past the S-wave with median NCC of 0.93 or higher across controlled geometries.
arXiv preprint arXiv:2402.16412 (2024) Seeing Without Eyes: 4D Human–Scene Understanding from Wearable IMUs 21
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IMU-to-4D uses wearable IMU data and repurposed LLMs to predict coherent 4D human motion plus coarse scene structure, outperforming cascaded state-of-the-art pipelines in temporal stability.
citing papers explorer
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Data-Driven Forecasting of three-Component Seismograms Using Transformer Architectures
A causal transformer trained on synthetic seismograms autoregressively continues three-component waveforms past the S-wave with median NCC of 0.93 or higher across controlled geometries.
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Seeing Without Eyes: 4D Human-Scene Understanding from Wearable IMUs
IMU-to-4D uses wearable IMU data and repurposed LLMs to predict coherent 4D human motion plus coarse scene structure, outperforming cascaded state-of-the-art pipelines in temporal stability.