Learning Local Features of Motion Chain for Human Motion Prediction

Extracting local features is a key technique in the field of human motion prediction. However, Due to incorrect partitioning of strongly correlated joint sets, existing methods ignore parts of strongly correlated joint pairs during local feature extraction, leading to prediction errors in end joints. In this paper, a Motion Chain Learning Framework is proposed to address the problem of prediction errors in end joints, such as hands and feet. The key idea is to mine and build strong correlations for joints belonging to the same motion chain. To be specific, all human joints are first divided into five parts according to the human motion chains. Then, the local interaction relationship between joints on each motion chain is learned by GCN. Finally, a novel Weights-Added Mean Per Joint Position Error loss function is proposed to assign different weights to each joint based on the importance in human biomechanics. Extensive evaluations demonstrate that our approach significantly outperforms state-of-the-art methods on the datasets such as H3.6M, CMU-Mocap, and 3DPW. Furthermore, the visual result confirms that our Motion Chain Learning Framework can reduce errors in end joints while working well for the other joints.