In 3D human action recognition, limited supervised data makes it challenging to fully tap into the modeling potential of powerful networks such as transformers. As a result, researchers have been actively investigating effective self-supervised pre-training strategies. In this work, we show that instead of following the prevalent pretext task to perform masked self-component reconstruction in human joints, explicit contextual motion modeling is key to the success of learning effective feature representation for 3D action recognition. Formally, we propose the Masked Motion Prediction (MAMP) framework. To be specific, the proposed MAMP takes as input the masked spatio-temporal skeleton sequence and predicts the corresponding temporal motion of the masked human joints. Considering the high temporal redundancy of the skeleton sequence, in our MAMP, the motion information also acts as an empirical semantic richness prior that guide the masking process, promoting better attention to semantically rich temporal regions. Extensive experiments on NTU-60, NTU-120, and PKU-MMD datasets show that the proposed MAMP pre-training substantially improves the performance of the adopted vanilla transformer, achieving state-of-the-art results without bells and whistles. The source code of our MAMP is available at https://github.com/maoyunyao/MAMP.

本文提出了一种用于3D动作识别的Masked Motion Prediction（MAMP）框架，通过对蒙面的空间-时间骨骼序列进行预测，实现对丰富语义区域的更好关注，进而提高自监督预训练的性能。经过对NTU-60、NTU-120和PKU-MMD数据集的广泛实验，表明所提出的MAMP预训练方法显著提高了基础的transformer模型的性能，并取得了最先进的结果。MAMP的源代码可以在给定的网址上找到。

面具化运动预测器是强大的三维动作表示学习器