Physics-Augmented Autoencoder for 3D Skeleton-Based Gait Recognition.

In this paper, we introduce physics-augmented autoencoder (PAA) framework for 3D skeleton-based human gait recognition. Specifically, we construct the autoencoder with a graph-convolution-based encoder and a physics-based decoder. The encoder takes the skeleton sequence as input and produces the generalized positions and forces of each joint, which are taken by the decoder to reconstruct the input skeleton based on the Lagrangian dynamics. In this way, the intermediate representations are physically plausible and discriminative. During the inference, the decoder is discared and a RNN-based classifier takes the output of the encoder for gait recognition. We evaluated our proposed method on three benchmark datasets including Gait3D, GREW, and KinectGait. Our method achieves state-of-the-art performance for 3D skeleton-based gait recognition. Furthermore, extensive ablation studies show that our method generalizes better and is more robust with small-scale training data by incorporating the physics knowledge. We also validated the physical plausibility of the intermediate representations by making force predictions on real data with physical annotations.