Skeleton-based action recognition has recently gained a lot of attention in computer vision. The previous skeleton-based datasets used sparse poses to represent the human body, which always leads to a large loss of human body detail information. Therefore, the previous skeleton-based methods generally performed worse than the image-based methods. In this paper, we propose a dense-pose-based action recognition dataset NTU-DensePose. This dataset automatically annotates 37,060 video samples with two dense poses, IUV equidistant annotation and IUV equivalent annotation. Each dense pose annotation contains more than 240 keypoints per instance. So the dense-pose-based action recognition method can capture more subtle details and predict human action more accurately than the previous skeleton-based methods. To the best of our knowledge, NTU-DensePose is the first dense-pose-based action recognition dataset.

In this paper, we propose an occluded face dataset named SOF (Synthetic Occluded Face) and describe in detail the construction method of SOF. We synthesize the occluded image into the face image after the thin plate spline deformation to obtain the occluded face image, and then make the image more real through guided filter. At the end of this paper, we use the SOF training set and general face datasets Ms-Celeb-1M, CASIA-WebFace to train the mainstream face recognition algorithms FaceNet, SphereFace and ArcFace, and test on a variety of test sets. A series of experiments proved that the occluded face dataset generated by this method could improve the accuracy of occluded face recognition of mainstream face recognition algorithms.

Currently available 2D skeleton datasets for action recognition mostly contain nonoccluded skeleton samples. Models trained on such datasets lack generalization ability in occlusion situations. In this paper we propose an occlusion projection method, which projects a 3D occlusion object into 2D plane to generate a 2D occluded area. Based on this method, we build a 2D occlusion skeleton dataset named OSD with 56,800 occluded skeleton samples and 60 distinct classes. Experimental results show that the model trained on OSD has better generalization ability in occlusion situations compared with the model trained on datasets with nonoccluded samples, which proves the effectiveness of OSD.