Deep Learning for 3D Human Motion Prediction: State-of-the-Art and Future Trends

Abstract

Due to the success of deep learning in wide range of computer vision and computer graphics tasks, there is an increasing number of developed methods leveraging deep neural networks to solve human motion prediction. Recent motion prediction methods focus on solving many issues to predict accurate and natural human motion in temporal domain. In this study, we present a comprehensive survey of deep-learning-based human motion prediction methods. First, we define the human motion prediction problem and the scope of this study. We then provide related background knowledge and a comprehensive list of motion prediction methods based on our proposed classification. Next, we provide a complete survey of the characteristics widely used in the literature and explain the evaluation processes. Finally, we presented a quantitative comparison of recent studies and address the remaining unsolved issues while exploring possible research directions for future research.