Parallel multi-stage rectification networks for 3D skeleton-based motion prediction

Jianqi Zhong

doi:10.1038/s41598-024-75782-7

Parallel multi-stage rectification networks for 3D skeleton-based motion prediction

2024-10-30

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DOI：10.1038/s41598-024-75782-7

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It is noted that Recurrent Neural Networks (RNNs), which are widely used in human prediction tasks, have achieved promising performance in motion prediction, owing to RNNs' robust capacity for spatial-temporal sequence modeling. However, RNN-based methods suffer from error accumulation due to their step-by-step prediction mechanism. Therefore, in this paper, we propose a three-stage parallel prediction network, which guides the output generation of these three networks with different objectives. In particular, we leverage the high-dimensional information in these three networks to fuse new information to generate the final output. In addition, we also designed a fusion block based on GRU and attention mechanism to extract high-dimensional information more efficiently. Extensive experiments show that our approach outperforms most recent methods in both short and long-term motion predictions on Human 3.6M, CMU Mocap, and 3DPW.

Keywords:

Recurrent neural network

Error accumulation

Motion prediction

Parallel multi-stage rectification networks for 3D skeleton-based motion prediction

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