Skeleton-based action recognition is a typical classification problem which plays a significant role in human-computer interaction and video understanding. Since a human skeleton has natural graphic features, methods based on graph convolutional networks (GCN) are widely applied in skeleton-based action recognition. Previous studies mainly focus on structural links in GCN to generate high-level features of human skeleton. However, low-level features are also important in many applications. For instance, lowlevel edge gradient and color information are important for image classificaion. This paper introduces a multi-branches structure to capture different low-level features of human skeleton. We combine both highlevel and low-level features to recognize human action. We validate our method in action recognition with two skeleton datasets, NTU-RGB+D and Kinetics. Experiment results indicate that the proposed method achieves considerable improvement over some state-of-the-art methods.
The development of 3D sensors encourages researchers to process point cloud data directly. Point cloud data requires less memory but conveys more detailed 3D shape information. However, because of occlusion, sensing distance and other reasons, sensors usually cannot get a complete 3D shape. In this paper, we propose a Dynamic and Folding Network (DF-Net) to address the precise point cloud completion problem. Existing completion networks generate the overall shape of a point cloud from an incomplete point cloud. In this paper, we complete the missing part based on the existing part instead. We use a dynamic graph network to better extract local features of points in the neighborhood points. A FoldBlock is used to refine the prediction of the missing part. We validate our method with two benchmarks, ShapeNet-13 and ShapeNet-55. Both qualitative and quantitative experimental results show the proposed method achieves improvement over some state-of-the-art methods. Code is available at https://github.com/yiqisetian/DF-Net.
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