Predicting and forecasting human dynamics is a very interesting but challenging task with several prospective applications in robotics, health-care, etc. Recently, several methods have been developed for human pose forecasting; however, they often introduce a number of limitations in their settings. For instance, previous work either focused only on short-term or long-term predictions, while sacrificing one or the other. Furthermore, they included the activity labels as part of the training process, and require them at testing time. These limitations confine the usage of pose forecasting models for real-world applications, as often there are no activity-related annotations for testing scenarios. In this paper, we propose a new action-agnostic method for short- and long-term human pose forecasting. To this end, we propose a new recurrent neural network for modeling the hierarchical and multi-scale characteristics of the human dynamics, denoted by triangular-prism RNN (TP-RNN). Our model captures the latent hierarchical structure embedded in temporal human pose sequences by encoding the temporal dependencies with different time-scales. For evaluation, we run an extensive set of experiments on Human 3.6M and Penn Action datasets and show that our method outperforms baseline and state-of-the-art methods quantitatively and qualitatively. Codes are available at https://github.com/eddyhkchiu/pose_forecast_wacv/

本文提出了一种新的不考虑活动标签的短期和长期人体姿势预测方法，使用一种新的循环神经网络模型——三角棱镜循环神经网络，该模型通过编码不同时间尺度的时间依赖性来捕获潜在的分层结构，经在Human 3.6M和Penn Action数据集上进行了广泛的实验，证明了本方法在数量和质量上均优于基线和最先进的方法。

动作无关的人体姿势预测