A coarse-to-fine autoregressive framework with multi-scale tokenization and scale-aware control reconstructs human motion from sparse observations and reports SOTA accuracy on AMASS.
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A framework for real-time ergonomic pose prediction from 3D volumetric video that trains personalized classifiers on user-labeled poses captured by RGB-D cameras.
citing papers explorer
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MotionMAR: Multi-scale Auto-Regressive Human Motion Reconstruction from Sparse Observations
A coarse-to-fine autoregressive framework with multi-scale tokenization and scale-aware control reconstructs human motion from sparse observations and reports SOTA accuracy on AMASS.
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A Machine Learning Framework for Real-Time Personalized Ergonomic Pose Analysis
A framework for real-time ergonomic pose prediction from 3D volumetric video that trains personalized classifiers on user-labeled poses captured by RGB-D cameras.