Abstract
AbstractHuman motion prediction is a fundamental problem in understanding human natural movements. This task is very challenging due to the complex human body constraints and diversity of action types. Due to the human body being a natural graph, graph convolutional network (GCN)‐based models perform better than the traditional recurrent neural network (RNN)‐based models on modeling the natural spatial and temporal dependencies lying in the motion data. In this paper, we develop the GCN‐based models further by adding densely connected links to increase their feature utilizations and address oversmoothing problem. More specifically, the GCN block is used to learn the spatial relationships between the nodes and each feature map of the GCN block propagates directly to every following block as input rather than residual linked. In this way, the spatial dependency of human motion data is exploited more sufficiently and the features of different level of scale are fused more efficiently. Extensive experiments demonstrate our model achieving the state‐of‐the‐art results on CMU dataset.
Highlights
Forecasting the future movements of human actions is a crucial topic in computer vision and computer graphics for its various practical applications in real life, such as surveillance,[1] pedestrian tracking,[2,3,4] interactive robotics,[5,6,7] and autonomous driving systems.[8]
We address the problem of generating human action movements in the 3D skeleton format
The nearest joints on the human body are vitally important for prediction movement. To address these limitations in a simple but effective way, we propose an advanced graph convolutional network (GCN) based framework for motion prediction which connects all the GCN blocks directly
Summary
Forecasting the future movements of human actions is a crucial topic in computer vision and computer graphics for its various practical applications in real life, such as surveillance,[1] pedestrian tracking,[2,3,4] interactive robotics,[5,6,7] and autonomous driving systems.[8] The data of human motions are usually captured by the Mocap system and represented in the format of the three-dimensional (3D) skeleton. We address the problem of generating human action movements in the 3D skeleton format. Most deep learning models treat motion prediction similar to machine translation problems and employ long short-term memory (LSTM)or convolutional neural network (CNN)-based models.[9,10,11,12,13,14,15,16] different from machine translation, motion data has special human body constraints and is a spatial-temporal data rather than temporal data. The LSTMs are Abbreviations: ANA, anti-nuclear antibodies, APC, antigen-presenting cells, IRF, interferon regulatory factor
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
