# MotionMixer: MLP-based 3D Human Body Pose Forecasting

arXiv: https://arxiv.org/abs/2207.00499 · HTML: https://mlsanity.com/paper/2207.00499/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 34 references that are in the map.
- First posted on arXiv 1 Jul 2022.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [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)
- [History Repeats Itself: Human Motion Prediction via Motion Attention](https://mlsanity.com/paper/2007.11755/) (2020)
- [A Spatio-temporal Transformer for 3D Human Motion Prediction](https://mlsanity.com/paper/2004.08692/) (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)
- [Space-Time-Separable Graph Convolutional Network for Pose Forecasting](https://mlsanity.com/paper/2110.04573/) (2021)
- [Spatio-Temporal Gating-Adjacency GCN for Human Motion Prediction](https://mlsanity.com/paper/2203.01474/) (2022)
- [Long-Term Human Motion Prediction by Modeling Motion Context and Enhancing Motion Dynamic](https://mlsanity.com/paper/1805.02513/) (2018)
- [Multi-level Motion Attention for Human Motion Prediction](https://mlsanity.com/paper/2106.09300/) (2021)
- [Traffic Control Gesture Recognition for Autonomous Vehicles](https://mlsanity.com/paper/2007.16072/) (2020)
- [Self-Supervised 3D Human Pose Estimation with Multiple-View Geometry](https://mlsanity.com/paper/2108.07777/) (2021)

## Built on by (most cited first, 30 of 42)

- [T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations](https://mlsanity.com/paper/2301.06052/) (2023)
- [Back to MLP: A Simple Baseline for Human Motion Prediction](https://mlsanity.com/paper/2207.01567/) (2022)
- [HumanMAC: Masked Motion Completion for Human Motion Prediction](https://mlsanity.com/paper/2302.03665/) (2023)
- [Multi-agent Long-term 3D Human Pose Forecasting via Interaction-aware Trajectory Conditioning](https://mlsanity.com/paper/2404.05218/) (2024)
- [Human Joint Kinematics Diffusion-Refinement for Stochastic Motion Prediction](https://mlsanity.com/paper/2210.05976/) (2022)
- [Interactive Humanoid: Online Full-Body Motion Reaction Synthesis with Social Affordance Canonicalization and Forecasting](https://mlsanity.com/paper/2312.08983/) (2023)
- [CoMusion: Towards Consistent Stochastic Human Motion Prediction via Motion Diffusion](https://mlsanity.com/paper/2305.12554/) (2023)
- [Diverse 3D Hand Gesture Prediction from Body Dynamics by Bilateral Hand Disentanglement](https://mlsanity.com/paper/2303.01765/) (2023)
- [TransFusion: A Practical and Effective Transformer-based Diffusion Model for 3D Human Motion Prediction](https://mlsanity.com/paper/2307.16106/) (2023)
- [EgoCast: Forecasting Egocentric Human Pose in the Wild](https://mlsanity.com/paper/2412.02903/) (2024)
- [T2M-HiFiGPT: Generating High Quality Human Motion from Textual Descriptions with Residual Discrete Representations](https://mlsanity.com/paper/2312.10628/) (2023)
- [MotionRL: Align Text-to-Motion Generation to Human Preferences with Multi-Reward Reinforcement Learning](https://mlsanity.com/paper/2410.06513/) (2024)
- [Best Practices for 2-Body Pose Forecasting](https://mlsanity.com/paper/2304.05758/) (2023)
- [Weakly Supervised Multi-Modal 3D Human Body Pose Estimation for Autonomous Driving](https://mlsanity.com/paper/2307.14889/) (2023)
- [Learning Generalizable Human Motion Generator with Reinforcement Learning](https://mlsanity.com/paper/2405.15541/) (2024)
- [Context-based Interpretable Spatio-Temporal Graph Convolutional Network for Human Motion Forecasting](https://mlsanity.com/paper/2402.19237/) (2024)
- [FutureHuman3D: Forecasting Complex Long-Term 3D Human Behavior from Video Observations](https://mlsanity.com/paper/2211.14309/) (2022)
- [DeFeeNet: Consecutive 3D Human Motion Prediction with Deviation Feedback](https://mlsanity.com/paper/2304.04496/) (2023)
- [Towards Accurate Human Motion Prediction via Iterative Refinement](https://mlsanity.com/paper/2305.04443/) (2023)
- [A Unified Masked Autoencoder with Patchified Skeletons for Motion Synthesis](https://mlsanity.com/paper/2308.07301/) (2023)
- [CacheFlow: Fast Human Motion Prediction by Cached Normalizing Flow](https://mlsanity.com/paper/2505.13140/) (2025)
- [Efficient Multi-Person Motion Prediction by Lightweight Spatial and Temporal Interactions](https://mlsanity.com/paper/2507.09446/) (2025)
- [Physics-constrained Attack against Convolution-based Human Motion Prediction](https://mlsanity.com/paper/2306.11990/) (2023)
- [Graph-Guided MLP-Mixer for Skeleton-Based Human Motion Prediction](https://mlsanity.com/paper/2304.03532/) (2023)
- [Persistent-Transient Duality: A Multi-mechanism Approach for Modeling Human-Object Interaction](https://mlsanity.com/paper/2307.12729/) (2023)
- [Spatio-temporal MLP-graph network for 3D human pose estimation](https://mlsanity.com/paper/2308.15313/) (2023)
- [Recent Advances in Deterministic Human Motion Prediction: A Review](https://mlsanity.com/paper/2312.06184/) (2023)
- [Fooling Neural Networks for Motion Forecasting via Adversarial Attacks](https://mlsanity.com/paper/2403.04954/) (2024)
- [Towards Efficient Large Scale Spatial-Temporal Time Series Forecasting via Improved Inverted Transformers](https://mlsanity.com/paper/2503.10858/) (2025)
- [Temporal Continual Learning with Prior Compensation for Human Motion Prediction](https://mlsanity.com/paper/2507.04060/) (2025)

## Abstract (arXiv)

In this work, we present MotionMixer, an efficient 3D human body pose forecasting model based solely on multi-layer perceptrons (MLPs). MotionMixer learns the spatial-temporal 3D body pose dependencies by sequentially mixing both modalities. Given a stacked sequence of 3D body poses, a spatial-MLP extracts fine grained spatial dependencies of the body joints. The interaction of the body joints over time is then modelled by a temporal MLP. The spatial-temporal mixed features are finally aggregated and decoded to obtain the future motion. To calibrate the influence of each time step in the pose sequence, we make use of squeeze-and-excitation (SE) blocks. We evaluate our approach on Human3.6M, AMASS, and 3DPW datasets using the standard evaluation protocols. For all evaluations, we demonstrate state-of-the-art performance, while having a model with a smaller number of parameters. Our code is available at: https://github.com/MotionMLP/MotionMixer