Imitation Learning for Human Pose Prediction

• Published in: 2019 IEEE/CVF International Conference on Computer Vision (ICCV) pp. 7123 - 7132
• Main Authors: Wang, Borui, Adeli, Ehsan, Chiu, Hsu-Kuang, Huang, De-An, Niebles, Juan Carlos
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-10-2019
• Subjects: Computer vision
• ISSN: 2380-7504
• DOI: 10.1109/ICCV.2019.00722

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.