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.

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Results from the Paper

Ranked #2 on Human Pose Forecasting on Human3.6M (MAR, walking, 1,000ms metric)

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Human Pose Forecasting Human3.6M BC+WGAIL-div MAR, walking, 400ms 0.59 # 5
MAR, walking, 1,000ms 0.69 # 2


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