Usman Muhammad, Zhong Jianqi
College of Electronics and Information Communication Engineering, Shenzhen University, Shenzhen, China.
Front Comput Neurosci. 2022 Oct 28;16:1051222. doi: 10.3389/fncom.2022.1051222. eCollection 2022.
Human motion prediction based on 3D skeleton data is an active research topic in computer vision and multimedia analysis, which involves many disciplines, such as image processing, pattern recognition, and artificial intelligence. As an effective representation of human motion, human 3D skeleton data is favored by researchers because it provide resistant to light effects, scene changes, etc. earlier studies on human motion prediction focuses mainly on RBG data-based techniques. In recent years, researchers have proposed the fusion of human skeleton data and depth learning methods for human motion prediction and achieved good results. We first introduced human motion prediction research background and significance in this survey. We then summarized the latest deep learning-based techniques for predicting human motion in recent years. Finally, a detailed paper review and future development discussion are provided.
基于3D骨骼数据的人体运动预测是计算机视觉和多媒体分析中一个活跃的研究课题，它涉及许多学科，如图像处理、模式识别和人工智能。作为人体运动的一种有效表示，人体3D骨骼数据受到研究人员的青睐，因为它能抵抗光照影响、场景变化等。早期关于人体运动预测的研究主要集中在基于RGB数据的技术上。近年来，研究人员提出将人体骨骼数据与深度学习方法融合用于人体运动预测，并取得了良好的成果。在本次综述中，我们首先介绍了人体运动预测的研究背景和意义。然后，我们总结了近年来基于深度学习的人体运动预测最新技术。最后，给出了详细的论文综述和对未来发展的讨论。
