MotionMAR: Multi-scale Auto-Regressive Human Motion Reconstruction from Sparse Observations
Human motion follows a temporal hierarchical structure, transitioning from low-frequency global trajectories to high-frequency details. Inspired by the success of multi-level autoregressive models in computer vision, we propose MotionMAR, a coarse-to-fine framework for motion reconstruction from sparse observations. It first estimates the global trajectory of human motion and then gradually refines the temporal details. This architecture consists of four integrated components. The Temporal Multi-scale Tokenization (TMT) VQ-VAE encodes the data at multiple temporal resolutions, separating semantic motion from minor jitters. The Motion Autoregressive Network (MAN) operates in this latent sp...
| Authors | Yuhua Luo, Junsheng Zhang, Mengyin Liu et al. |
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| Primary category | cs.CV |
| Published | 22 June 2026 |
| Last updated at source | 6 August 2026 |
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