Learning Multiscale Correlations for Human Motion Prediction
This work addresses the problem of accurate human motion prediction for applications like robotics and animation, representing an incremental improvement with specific gains in complex actions.
The paper tackles the challenge of predicting aperiodic and complicated human motions by proposing a multiscale graph convolution network (MGCN) to capture correlations among body components, achieving state-of-the-art performance on Human3.6M and CMU datasets for both short-term and long-term predictions.
In spite of the great progress in human motion prediction, it is still a challenging task to predict those aperiodic and complicated motions. We believe that to capture the correlations among human body components is the key to understand the human motion. In this paper, we propose a novel multiscale graph convolution network (MGCN) to address this problem. Firstly, we design an adaptive multiscale interactional encoding module (MIEM) which is composed of two sub modules: scale transformation module and scale interaction module to learn the human body correlations. Secondly, we apply a coarse-to-fine decoding strategy to decode the motions sequentially. We evaluate our approach on two standard benchmark datasets for human motion prediction: Human3.6M and CMU motion capture dataset. The experiments show that the proposed approach achieves the state-of-the-art performance for both short-term and long-term prediction especially in those complicated action category.