We present an attention-based model that reasons on human body shape and motion dynamics to identify individuals in the absence of RGB information, hence in the dark. Our approach leverages unique 4D spatio-temporal signatures to address the identification problem across days. Formulated as a reinforcement learning task, our model is based on a combination of convolutional and recurrent neural networks with the goal of identifying small, discriminative regions indicative of human identity. We demonstrate that our model produces state-of-the-art results on several published datasets given only depth images. We further study the robustness of our model towards viewpoint, appearance, and volumetric changes. Finally, we share insights gleaned from interpretable 2D, 3D, and 4D visualizations of our model's spatio-temporal attention.

本文提出了一种基于注意力机制的人体形状和运动动力学的识别模型，用于在没有RGB信息的情况下对人员身份进行识别。该模型利用独特的4D时空特征，采用卷积和循环神经网络相结合的形式，目标是识别代表人类身份的小的、具有区分性的区域。结果表明，该模型能够在多个公开数据集上产生当今最先进的结果。同时，本文还对该模型在视角、外貌和容积变化方面的鲁棒性进行了研究，并分享了该模型时空注意力的可解释的可视化结果。

基于深度的人物识别的递归注意模型