HCMLP: A Highly Condensed All-MLP Architecture for Extended Long-term Human Motion Prediction

Accurate human motion prediction has significant potential in various artificial intelligence applications. To accommodate the demands of applications such as autonomous driving on mobile devices, it is essential to utilize models that are both lightweight and capable of performing extended-duration predictions to ensure the system remains swift and reliable. To address these challenges, we present the HCMLP, a highly condensed all-MLP architecture designed for optimal lightweight efficiency, enabling extended long-term predictions without compromising performance. This pioneering method simultaneously captures the spatial correlations between pose joints and the temporal dynamics of each joint by employing distinct but parallel spatial and temporal MLPs. Then, Dynamic Aggregation component dynamically assimilates the spatial and temporal correlations. Finally, channel MLP synergizes and refines these spatio-temporal features for enhanced prediction accuracy. Our experiments on the Human3.6M, AMASS, and 3DPW datasets reveal that HCMLP surpasses the performance of current state-of-the-art methods in short-term, long-term, and particularly extended long-term predictions, while maintaining the least parameters. Code will be available at https://github.com/alanzhangv123/HCMLP.