Papers › Imitation Learning for Human Pose Prediction

Imitation Learning for Human Pose Prediction

8 Sep 2019ICCV 2019 10arXiv:1909.03449archive 2025-07-28

Borui Wang, Ehsan Adeli, Hsu-kuang Chiu, De-An Huang, Juan Carlos Niebles

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.

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Tasks

Deep Reinforcement Learning Human Pose Forecasting Imitation Learning Pose Prediction Prediction Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Human Pose Forecasting	Human3.6M	BC+WGAIL-div	MAR, walking, 1,000ms	0.69	#18 of 33	Archive leaderboard	report
Human Pose Forecasting	Human3.6M	BC+WGAIL-div	MAR, walking, 400ms	0.59	#18 of 33	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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