LC-Flow introduces a continuous local recurrent network for learning sparse optical flow and confidence directly from event streams, with confidence-guided aggregation reaching new SOTA on MVSEC.
Continuous-Time Human Motion Field from Events
2 Pith papers cite this work. Polarity classification is still indexing.
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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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LC-Flow: Learning Local Continuous Optical Flow and Confidence from events
LC-Flow introduces a continuous local recurrent network for learning sparse optical flow and confidence directly from event streams, with confidence-guided aggregation reaching new SOTA on MVSEC.
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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.