  Human skeletons and RGB sequences are both widely-adopted input modalities
for human action recognition. However, skeletons lack appearance features and
color data suffer large amount of irrelevant depiction. To address this, we
introduce human parsing feature map as a novel modality, since it can
selectively retain spatiotemporal features of the body parts, while filtering
out noises regarding outfits, backgrounds, etc. We propose an Integrating Human
Parsing and Pose Network (IPP-Net) for action recognition, which is the first
to leverage both skeletons and human parsing feature maps in dual-branch
approach. The human pose branch feeds compact skeletal representations of
different modalities in graph convolutional network to model pose features. In
human parsing branch, multi-frame body-part parsing features are extracted with
human detector and parser, which is later learnt using a convolutional
backbone. A late ensemble of two branches is adopted to get final predictions,
considering both robust keypoints and rich semantic body-part features.
Extensive experiments on NTU RGB+D and NTU RGB+D 120 benchmarks consistently
verify the effectiveness of the proposed IPP-Net, which outperforms the
existing action recognition methods. Our code is publicly available at
https://github.com/liujf69/IPP-Net-Parsing .
