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Long-Term Human Motion Prediction by Modeling Motion Context and Enhancing Motion Dynamic

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arxiv 1805.02513 v1 pith:LSGSVESN submitted 2018-05-07 cs.CV

Yongyi Tang , Lin Ma , Wei Liu , Weishi Zheng This is my paper · ORCID
classification cs.CV
keywords motionhumanpredictioncontextdynamicfuturehistoricallong-term
verification ladder T0 review T1 audit T2 compute T3 formal

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Human motion prediction aims at generating future frames of human motion based on an observed sequence of skeletons. Recent methods employ the latest hidden states of a recurrent neural network (RNN) to encode the historical skeletons, which can only address short-term prediction. In this work, we propose a motion context modeling by summarizing the historical human motion with respect to the current prediction. A modified highway unit (MHU) is proposed for efficiently eliminating motionless joints and estimating next pose given the motion context. Furthermore, we enhance the motion dynamic by minimizing the gram matrix loss for long-term motion prediction. Experimental results show that the proposed model can promisingly forecast the human future movements, which yields superior performances over related state-of-the-art approaches. Moreover, specifying the motion context with the activity labels enables our model to perform human motion transfer.

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GGMotion: Group Graph Dynamics-Kinematics Networks for Human Motion Prediction

    cs.CV 2025-07 conditional novelty 6.0 of 10

    GGMotion, a grouped equivariant graph network with spatio-temporal radial fields and a parallel dynamics-kinematics update, reports the lowest average MPJPE among compared baselines on Human3.6M, CMU-Mocap, and 3DPW f...

  2. Stochastic Human Motion Prediction with Memory of Action Transition and Action Characteristic

    cs.CV 2025-07 conditional novelty 6.0 of 10

    Adding a soft-transition action bank, an action characteristic bank, and adaptive attention fusion to the WAT baseline improves action-conditioned human motion prediction on four benchmarks.

  3. Spatio-Temporal Multi-Subgraph GCN for 3D Human Motion Prediction

    cs.CV 2024-12 conditional novelty 4.0 of 10

    STMS-GCN improves average MPJPE by roughly 3 percent over listed baselines on Human3.6M and CMU Mocap.

  4. 3D Skeleton-Based Action Recognition: A Review

    cs.CV 2025-06 reject novelty 3.0 of 10

    A task-oriented review of skeleton-based action recognition that reorganizes known methods along a data processing pipeline and contains no new experimental result.

  5. PISEP^2: Pseudo Image Sequence Evolution based 3D Pose Prediction

    cs.CV 2019-09 conditional novelty 3.0 of 10

    A non-recursive encoder-dynamics-decoder network, fed with 3D joint coordinates rearranged into small pseudo-images, predicts future poses and outperforms two baselines on G3D and a filtered NTU dataset.

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