# Skeleton-Parted Graph Scattering Networks for 3D Human Motion Prediction

arXiv: https://arxiv.org/abs/2208.00368 · HTML: https://mlsanity.com/paper/2208.00368/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 67 references that are in the map.
- First posted on arXiv 31 Jul 2022.

## Builds on (most cited first, 30 of 45)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Neural Relational Inference for Interacting Systems](https://mlsanity.com/paper/1802.04687/) (2018)
- [On human motion prediction using recurrent neural networks](https://mlsanity.com/paper/1705.02445/) (2017)
- [Learning Convolutional Neural Networks for Graphs](https://mlsanity.com/paper/1605.05273/) (2016)
- [Learning Trajectory Dependencies for Human Motion Prediction](https://mlsanity.com/paper/1908.05436/) (2019)
- [Structural-RNN: Deep Learning on Spatio-Temporal Graphs](https://mlsanity.com/paper/1511.05298/) (2015)
- [Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks](https://mlsanity.com/paper/1907.03395/) (2019)
- [Learning Human-Object Interactions by Graph Parsing Neural Networks](https://mlsanity.com/paper/1808.07962/) (2018)
- [History Repeats Itself: Human Motion Prediction via Motion Attention](https://mlsanity.com/paper/2007.11755/) (2020)
- [Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction](https://mlsanity.com/paper/2003.08802/) (2020)
- [Discriminative Embeddings of Latent Variable Models for Structured Data](https://mlsanity.com/paper/1603.05629/) (2016)
- [GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning](https://mlsanity.com/paper/2204.08770/) (2022)
- [Video Object Segmentation with Episodic Graph Memory Networks](https://mlsanity.com/paper/2007.07020/) (2020)
- [Convolutional Sequence to Sequence Model for Human Dynamics](https://mlsanity.com/paper/1805.00655/) (2018)
- [MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction](https://mlsanity.com/paper/2108.07152/) (2021)
- [The Pose Knows: Video Forecasting by Generating Pose Futures](https://mlsanity.com/paper/1705.00053/) (2017)
- [Space-Time-Separable Graph Convolutional Network for Pose Forecasting](https://mlsanity.com/paper/2110.04573/) (2021)
- [Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks](https://mlsanity.com/paper/2003.08414/) (2020)
- [Collaborative Motion Prediction via Neural Motion Message Passing](https://mlsanity.com/paper/2003.06594/) (2020)
- [Deep Scattering Spectrum](https://mlsanity.com/paper/1304.6763/) (2013)
- [Geometric Scattering for Graph Data Analysis](https://mlsanity.com/paper/1810.03068/) (2018)
- [Graph Convolutional Neural Networks via Scattering](https://mlsanity.com/paper/1804.00099/) (2018)
- [Diffusion Scattering Transforms on Graphs](https://mlsanity.com/paper/1806.08829/) (2018)

## Built on by (most cited first, 29 of 29)

- [EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning](https://mlsanity.com/paper/2303.10876/) (2023)
- [Trajectory-Aware Body Interaction Transformer for Multi-Person Pose Forecasting](https://mlsanity.com/paper/2303.05095/) (2023)
- [CoMusion: Towards Consistent Stochastic Human Motion Prediction via Motion Diffusion](https://mlsanity.com/paper/2305.12554/) (2023)
- [Auxiliary Tasks Benefit 3D Skeleton-based Human Motion Prediction](https://mlsanity.com/paper/2308.08942/) (2023)
- [Diverse 3D Hand Gesture Prediction from Body Dynamics by Bilateral Hand Disentanglement](https://mlsanity.com/paper/2303.01765/) (2023)
- [The Invisible EgoHand: 3D Hand Forecasting through EgoBody Pose Estimation](https://mlsanity.com/paper/2504.08654/) (2025)
- [Another Vertical View: A Hierarchical Network for Heterogeneous Trajectory Prediction via Spectrums](https://mlsanity.com/paper/2304.05106/) (2023)
- [Spatio-Temporal Branching for Motion Prediction using Motion Increments](https://mlsanity.com/paper/2308.01097/) (2023)
- [Human Part-wise 3D Motion Context Learning for Sign Language Recognition](https://mlsanity.com/paper/2308.09305/) (2023)
- [Multimodal Sense-Informed Prediction of 3D Human Motions](https://mlsanity.com/paper/2405.02911/) (2024)
- [Frequency-Semantic Enhanced Variational Autoencoder for Zero-Shot Skeleton-based Action Recognition](https://mlsanity.com/paper/2506.22179/) (2025)
- [A Spatio-Temporal Representation Learning as an Alternative to Traditional Glosses in Sign Language Translation and Production](https://mlsanity.com/paper/2407.02854/) (2024)
- [HUMOF: Human Motion Forecasting in Interactive Social Scenes](https://mlsanity.com/paper/2506.03753/) (2025)
- [Graph-Guided MLP-Mixer for Skeleton-Based Human Motion Prediction](https://mlsanity.com/paper/2304.03532/) (2023)
- [PGformer: Proxy-Bridged Game Transformer for Multi-Person Highly Interactive Extreme Motion Prediction](https://mlsanity.com/paper/2306.03374/) (2023)
- [Recent Advances in Deterministic Human Motion Prediction: A Review](https://mlsanity.com/paper/2312.06184/) (2023)
- [Off-the-shelf ChatGPT is a Good Few-shot Human Motion Predictor](https://mlsanity.com/paper/2405.15267/) (2024)
- [Spatio-Temporal Multi-Subgraph GCN for 3D Human Motion Prediction](https://mlsanity.com/paper/2501.00317/) (2024)
- [Reverberation: Learning the Latencies Before Forecasting Trajectories](https://mlsanity.com/paper/2511.11164/) (2025)
- [Personalized Pose Forecasting](https://mlsanity.com/paper/2312.03528/) (2023)
- [Multi-Scale Incremental Modeling for Enhanced Human Motion Prediction in Human-Robot Collaboration](https://mlsanity.com/paper/2412.11632/) (2024)
- [Temporal Dynamics Decoupling with Inverse Processing for Enhancing Human Motion Prediction](https://mlsanity.com/paper/2501.00315/) (2024)
- [Enhancing Human Motion Prediction via Multi-range Decoupling Decoding with Gating-adjusting Aggregation](https://mlsanity.com/paper/2503.23381/) (2025)
- [GGMotion: Group Graph Dynamics-Kinematics Networks for Human Motion Prediction](https://mlsanity.com/paper/2507.07515/) (2025)
- [LuKAN: A Kolmogorov-Arnold Network Framework for 3D Human Motion Prediction](https://mlsanity.com/paper/2508.04847/) (2025)
- [Scriboora: Rethinking Human Pose Forecasting](https://mlsanity.com/paper/2511.15565/) (2025)
- [SMamDiff: Spatial Mamba for Stochastic Human Motion Prediction](https://mlsanity.com/paper/2512.00355/) (2025)
- [AtomicMotion: Learning Human Motion From Different Human Parts](https://mlsanity.com/paper/2605.22631/) (2026)
- [Capturing Uncertainty in Human Motion for Representation Learning in Soccer](https://mlsanity.com/paper/2608.11203/) (2026)

## Abstract (arXiv)

Graph convolutional network based methods that model the body-joints' relations, have recently shown great promise in 3D skeleton-based human motion prediction. However, these methods have two critical issues: first, deep graph convolutions filter features within only limited graph spectrums, losing sufficient information in the full band; second, using a single graph to model the whole body underestimates the diverse patterns on various body-parts. To address the first issue, we propose adaptive graph scattering, which leverages multiple trainable band-pass graph filters to decompose pose features into richer graph spectrum bands. To address the second issue, body-parts are modeled separately to learn diverse dynamics, which enables finer feature extraction along the spatial dimensions. Integrating the above two designs, we propose a novel skeleton-parted graph scattering network (SPGSN). The cores of the model are cascaded multi-part graph scattering blocks (MPGSBs), building adaptive graph scattering on diverse body-parts, as well as fusing the decomposed features based on the inferred spectrum importance and body-part interactions. Extensive experiments have shown that SPGSN outperforms state-of-the-art methods by remarkable margins of 13.8%, 9.3% and 2.7% in terms of 3D mean per joint position error (MPJPE) on Human3.6M, CMU Mocap and 3DPW datasets, respectively.