Introduces LUMINA-26 low-light action dataset and Illumi-Net model achieving 75.95% Top-1 accuracy on it while surpassing prior SOTA on ELLAR.
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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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LUMINA-26: Low-Light Understanding for Modeling and Interpreting Night-time Actions
Introduces LUMINA-26 low-light action dataset and Illumi-Net model achieving 75.95% Top-1 accuracy on it while surpassing prior SOTA on ELLAR.
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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.