  Human motion prediction based on 3D skeleton is a significant challenge in
computer vision, primarily focusing on the effective representation of motion.
In this paper, we propose a self-supervised learning framework designed to
enhance motion representation. This framework consists of two stages: first,
the network is pretrained through the self-reconstruction of past sequences,
and the guided reconstruction of future sequences based on past movements. We
design a velocity-based mask strategy to focus on the joints with large-scale
moving. Subsequently, the pretrained network undergoes finetuning for specific
tasks. Self-reconstruction, guided by patterns of past motion, substantially
improves the model's ability to represent the spatiotemporal relationships
among joints but also captures the latent relationships between past and future
sequences. This capability is crucial for motion prediction tasks that solely
depend on historical motion data. By employing this straightforward yet
effective training paradigm, our method outperforms existing
\textit{state-of-the-art} methods, reducing the average prediction errors by
8.8\% across Human3.6M, 3DPW, and AMASS datasets. The code is available at
https://github.com/JunyuShi02/PMG-MRL.
