From Sparse Signal to Smooth Motion: Real-Time Motion Generation with Rolling Prediction Models
Germán Barquero, Nadine Bertsch, Manojkumar Marramreddy, Carlos Chacón, Filippo Arcadu, Ferran Rigual, Nicky Sijia He, Cristina Palmero, Sergio Escalera, Yuting Ye, Robin Kips
2025Year
4Top-tier citations
Abstract
Figure 1. We introduce Rolling Prediction Model, an approach that generates smooth and realistic full-body human motion in the two of the most common XR sensing signals: hand controllers, in which the tracking signal is always available (left), and hand tracking, in which the tracking signal is noisy and might be lost for long periods of time (right). Tracking input trajectories are shown as magenta lines.
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Cited by top-tier papers4
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- FisherPoser: Human Motion Estimation from Sparse Observations with Hierarchical Region-Wise Fisher-Matrix Uncertainty ModelingSongpengcheng Xia, Qingyu Zhang, Zhuo Su, Jiarui Yang et al.CVPR 2026
- KineST: A Kinematics-guided Spatiotemporal State Space Model for Human Motion Tracking from Sparse SignalsShuting Zhao, Zeyu Xiao, Xinrong ChenAAAI 2026
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- Human Motion Diffusion as a Generative PriorYoni Shafir, Guy Tevet, Roy Kapon, Amit Haim BermanoICLR 2024 · 371 citations
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