mlsanityData to 7 Oct 2026

Simple Baseline for Single Human Motion Forecasting

arXiv 2110.07495 ↗First posted 14 Oct 2021cs.CVcited by 18

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Abstract (arXiv)

Global human motion forecasting is important in many fields, which is the combination of global human trajectory prediction and local human pose prediction. Visual and social information are often used to boost model performance, however, they may consume too much computational resource. In this paper, we establish a simple but effective baseline for single human motion forecasting without visual and social information, equipped with useful training tricks. Our method "futuremotion_ICCV21" outperforms existing methods by a large margin on SoMoF benchmark. We hope our work provide new ideas for future research.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 15 of its 20 references that are in the map. First posted on arXiv 14 Oct 2021.

Builds on

15 of 20 references.

References · 15

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2019AMASS: Archive of Motion Capture as Surface Shapescited by 1,208
  4. 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 687
  5. 2017Cascaded Pyramid Network for Multi-Person Pose Estimationcited by 341
  6. 2016RMPE: Regional Multi-person Pose Estimationcited by 340
  7. 2017On human motion prediction using recurrent neural networkscited by 336
  8. 2019Learning Trajectory Dependencies for Human Motion Predictioncited by 245
  9. 2018CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmarkcited by 214
  10. 2017PoseTrack: A Benchmark for Human Pose Estimation and Trackingcited by 173
  11. 2020History Repeats Itself: Human Motion Prediction via Motion Attentioncited by 164
  12. 2018Pose Flow: Efficient Online Pose Trackingcited by 80
3 more
  1. 2020PaStaNet: Toward Human Activity Knowledge Enginecited by 79
  2. 2021TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wildcited by 52
  3. 2020Socially and Contextually Aware Human Motion and Pose Forecastingcited by 51

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 4; 2024: 4; 2025: 2; 2026: 2.

18 papers.

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  1. 2022Spatio-Temporal Gating-Adjacency GCN for Human Motion Predictioncited by 48
  2. 2024Multi-agent Long-term 3D Human Pose Forecasting via Interaction-aware Trajectory Conditioningcited by 29
  3. 2023Stochastic Multi-Person 3D Motion Forecastingcited by 28
  4. 2022Pose Forecasting in Industrial Human-Robot Collaborationcited by 24
  5. 2022SoMoFormer: Multi-Person Pose Forecasting with Transformerscited by 23
  6. 2023Joint-Relation Transformer for Multi-Person Motion Predictioncited by 16
  7. 2022Didn't see that coming: a survey on non-verbal social human behavior forecastingcited by 12
  8. 2025Nonisotropic Gaussian Diffusion for Realistic 3D Human Motion Predictioncited by 12
  9. 2022Comparison of Spatio-Temporal Models for Human Motion and Pose Forecasting in Face-to-Face Interaction Scenarioscited by 8
  10. 2023Social Motion Prediction with Cognitive Hierarchiescited by 8
  11. 2023The MI-Motion Dataset and Benchmark for 3D Multi-Person Motion Predictioncited by 3
  12. 2022Context Attention Network for Skeleton Extractioncited by 1
6 more
  1. 2025UPTor: Unified 3D Human Pose Dynamics and Trajectory Prediction for Human-Robot Interactioncited by 1
  2. 2026PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecastingcited by 1
  3. 2024Social-MAE: Social Masked Autoencoder for Multi-person Motion Representation Learningcited by 0
  4. 2024Relation Learning and Aggregate-attention for Multi-person Motion Predictioncited by 0
  5. 2024UnityGraph: Unified Learning of Spatio-temporal features for Multi-person Motion Predictioncited by 0
  6. 2026Ego-Human Motion Prediction with 3D-Aware LLMcited by 0