Imitation Learning for Human Pose Prediction
Modeling and prediction of human motion dynamics has long been a challenging problem in computer vision, and most existing methods rely on the end-to-end supervised training of various architectures of recurrent neural networks. Inspired by the recent success of deep reinforcement learning methods, in this paper we propose a new reinforcement learning formulation for the problem of human pose prediction, and develop an imitation learning algorithm for predicting future poses under this formulation through a combination of behavioral cloning and generative adversarial imitation learning. Our experiments show that our proposed method outperforms all existing state-of-the-art baseline models by large margins on the task of human pose prediction in both short-term predictions and long-term predictions, while also enjoying huge advantage in training speed.
PDF Abstract ICCV 2019 PDF ICCV 2019 Abstract
Datasets
Human3.6M
Results from the Paper
Ranked #2 on
Human Pose Forecasting
on Human3.6M
(MAR, walking, 1,000ms metric)
