NTU-DensePose: A New Benchmark for Dense Pose Action Recognition

Mengmeng Duan, Haoyue Qiu, Zimo Zhang, Yuan Wu

Abstract: 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.

External IDs:dblp:conf/bigdataconf/DuanQZW21