# Multi-Scale Incremental Modeling for Enhanced Human Motion Prediction in Human-Robot Collaboration

arXiv: https://arxiv.org/abs/2412.11632 · HTML: https://mlsanity.com/paper/2412.11632/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 37 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Builds on (most cited first, 22 of 22)

- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [On human motion prediction using recurrent neural networks](https://mlsanity.com/paper/1705.02445/) (2017)
- [Learning Trajectory Dependencies for Human Motion Prediction](https://mlsanity.com/paper/1908.05436/) (2019)
- [Long-term Human Motion Prediction with Scene Context](https://mlsanity.com/paper/2007.03672/) (2020)
- [Convolutional Sequence to Sequence Model for Human Dynamics](https://mlsanity.com/paper/1805.00655/) (2018)
- [Back to MLP: A Simple Baseline for Human Motion Prediction](https://mlsanity.com/paper/2207.01567/) (2022)
- [Space-Time-Separable Graph Convolutional Network for Pose Forecasting](https://mlsanity.com/paper/2110.04573/) (2021)
- [Physics-based Human Motion Estimation and Synthesis from Videos](https://mlsanity.com/paper/2109.09913/) (2021)
- [Progressively Generating Better Initial Guesses Towards Next Stages for High-Quality Human Motion Prediction](https://mlsanity.com/paper/2203.16051/) (2022)
- [MotionRNN: A Flexible Model for Video Prediction with Spacetime-Varying Motions](https://mlsanity.com/paper/2103.02243/) (2021)
- [Pose Transformers (POTR): Human Motion Prediction with Non-Autoregressive Transformers](https://mlsanity.com/paper/2109.07531/) (2021)
- [MotionMixer: MLP-based 3D Human Body Pose Forecasting](https://mlsanity.com/paper/2207.00499/) (2022)
- [Multi-level Motion Attention for Human Motion Prediction](https://mlsanity.com/paper/2106.09300/) (2021)
- [Skeleton-Parted Graph Scattering Networks for 3D Human Motion Prediction](https://mlsanity.com/paper/2208.00368/) (2022)
- [PVRED: A Position-Velocity Recurrent Encoder-Decoder for Human Motion Prediction](https://mlsanity.com/paper/1906.06514/) (2019)
- [MotionAug: Augmentation with Physical Correction for Human Motion Prediction](https://mlsanity.com/paper/2203.09116/) (2022)
- [Spatio-Temporal Branching for Motion Prediction using Motion Increments](https://mlsanity.com/paper/2308.01097/) (2023)
- [Learning Constrained Dynamic Correlations in Spatiotemporal Graphs for Motion Prediction](https://mlsanity.com/paper/2204.01297/) (2022)
- [Long Term Motion Prediction Using Keyposes](https://mlsanity.com/paper/2012.04731/) (2020)
- [DeFeeNet: Consecutive 3D Human Motion Prediction with Deviation Feedback](https://mlsanity.com/paper/2304.04496/) (2023)
- [3D Skeleton-based Human Motion Prediction with Manifold-Aware GAN](https://mlsanity.com/paper/2203.00736/) (2022)
- [Multiscale Residual Learning of Graph Convolutional Sequence Chunks for Human Motion Prediction](https://mlsanity.com/paper/2308.16801/) (2023)

## Abstract (arXiv)

Accurate human motion prediction is crucial for safe human-robot collaboration but remains challenging due to the complexity of modeling intricate and variable human movements. This paper presents Parallel Multi-scale Incremental Prediction (PMS), a novel framework that explicitly models incremental motion across multiple spatio-temporal scales to capture subtle joint evolutions and global trajectory shifts. PMS encodes these multi-scale increments using parallel sequence branches, enabling iterative refinement of predictions. A multi-stage training procedure with a full-timeline loss integrates temporal context. Extensive experiments on four datasets demonstrate substantial improvements in continuity, biomechanical consistency, and long-term forecast stability by modeling inter-frame increments. PMS achieves state-of-the-art performance, increasing prediction accuracy by 16.3%-64.2% over previous methods. The proposed multi-scale incremental approach provides a powerful technique for advancing human motion prediction capabilities critical for seamless human-robot interaction.