# A Neural Temporal Model for Human Motion Prediction

arXiv: https://arxiv.org/abs/1809.03036 · HTML: https://mlsanity.com/paper/1809.03036/

## Key facts

- Cited by 87 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2018.

## Built on by (most cited first, 30 of 87)

- [PhysDiff: Physics-Guided Human Motion Diffusion Model](https://mlsanity.com/paper/2212.02500/) (2022)
- [Robust Motion In-betweening](https://mlsanity.com/paper/2102.04942/) (2021)
- [TEACH: Temporal Action Composition for 3D Humans](https://mlsanity.com/paper/2209.04066/) (2022)
- [GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras](https://mlsanity.com/paper/2112.01524/) (2021)
- [GOAL: Generating 4D Whole-Body Motion for Hand-Object Grasping](https://mlsanity.com/paper/2112.11454/) (2021)
- [CG-HOI: Contact-Guided 3D Human-Object Interaction Generation](https://mlsanity.com/paper/2311.16097/) (2023)
- [DanceFormer: Music Conditioned 3D Dance Generation with Parametric Motion Transformer](https://mlsanity.com/paper/2103.10206/) (2021)
- [Human Motion Generation: A Survey](https://mlsanity.com/paper/2307.10894/) (2023)
- [Generating Smooth Pose Sequences for Diverse Human Motion Prediction](https://mlsanity.com/paper/2108.08422/) (2021)
- [Progressively Generating Better Initial Guesses Towards Next Stages for High-Quality Human Motion Prediction](https://mlsanity.com/paper/2203.16051/) (2022)
- [Large Motion Model for Unified Multi-Modal Motion Generation](https://mlsanity.com/paper/2404.01284/) (2024)
- [Multi-Person 3D Motion Prediction with Multi-Range Transformers](https://mlsanity.com/paper/2111.12073/) (2021)
- [Single-Shot Motion Completion with Transformer](https://mlsanity.com/paper/2103.00776/) (2021)
- [The Wanderings of Odysseus in 3D Scenes](https://mlsanity.com/paper/2112.09251/) (2021)
- [CAMS: CAnonicalized Manipulation Spaces for Category-Level Functional Hand-Object Manipulation Synthesis](https://mlsanity.com/paper/2303.15469/) (2023)
- [Interactive Character Control with Auto-Regressive Motion Diffusion Models](https://mlsanity.com/paper/2306.00416/) (2023)
- [Spatio-Temporal Gating-Adjacency GCN for Human Motion Prediction](https://mlsanity.com/paper/2203.01474/) (2022)
- [Task-Oriented Human-Object Interactions Generation with Implicit Neural Representations](https://mlsanity.com/paper/2303.13129/) (2023)
- [Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction](https://mlsanity.com/paper/2108.11244/) (2021)
- [RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting](https://mlsanity.com/paper/2108.01316/) (2021)
- [Multi-level Motion Attention for Human Motion Prediction](https://mlsanity.com/paper/2106.09300/) (2021)
- [PACE: Human and Camera Motion Estimation from in-the-wild Videos](https://mlsanity.com/paper/2310.13768/) (2023)
- [3D Human Motion Prediction: A Survey](https://mlsanity.com/paper/2203.01593/) (2022)
- [Conditional Neural Relational Inference for Interacting Systems](https://mlsanity.com/paper/2106.11083/) (2021)
- [Contact-aware Human Motion Forecasting](https://mlsanity.com/paper/2210.03954/) (2022)
- [Weakly-supervised Action Transition Learning for Stochastic Human Motion Prediction](https://mlsanity.com/paper/2205.15608/) (2022)
- [Plan, Posture and Go: Towards Open-World Text-to-Motion Generation](https://mlsanity.com/paper/2312.14828/) (2023)
- [Pose Forecasting in Industrial Human-Robot Collaboration](https://mlsanity.com/paper/2208.07308/) (2022)
- [HMP: Hand Motion Priors for Pose and Shape Estimation from Video](https://mlsanity.com/paper/2312.16737/) (2023)
- [Harmon: Whole-Body Motion Generation of Humanoid Robots from Language Descriptions](https://mlsanity.com/paper/2410.12773/) (2024)

## Abstract (arXiv)

We propose novel neural temporal models for predicting and synthesizing human motion, achieving state-of-the-art in modeling long-term motion trajectories while being competitive with prior work in short-term prediction and requiring significantly less computation. Key aspects of our proposed system include: 1) a novel, two-level processing architecture that aids in generating planned trajectories, 2) a simple set of easily computable features that integrate derivative information, and 3) a novel multi-objective loss function that helps the model to slowly progress from simple next-step prediction to the harder task of multi-step, closed-loop prediction. Our results demonstrate that these innovations improve the modeling of long-term motion trajectories. Finally, we propose a novel metric, called Normalized Power Spectrum Similarity (NPSS), to evaluate the long-term predictive ability of motion synthesis models, complementing the popular mean-squared error (MSE) measure of Euler joint angles over time. We conduct a user study to determine if the proposed NPSS correlates with human evaluation of long-term motion more strongly than MSE and find that it indeed does. We release code and additional results (visualizations) for this paper at: https://github.com/cr7anand/neural_temporal_models