# Integrating Human Parsing and Pose Network for Human Action Recognition

arXiv: https://arxiv.org/abs/2307.07977 · HTML: https://mlsanity.com/paper/2307.07977/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 33 references that are in the map.
- First posted on arXiv 16 Jul 2023.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis](https://mlsanity.com/paper/1604.02808/) (2016)
- [NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding](https://mlsanity.com/paper/1905.04757/) (2019)
- [Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2003.14111/) (2020)
- [Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2107.12213/) (2021)
- [Self-Correction for Human Parsing](https://mlsanity.com/paper/1910.09777/) (2019)
- [Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks](https://mlsanity.com/paper/2303.17602/) (2023)
- [Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action Recognition](https://mlsanity.com/paper/2007.14690/) (2020)
- [Look into Person: Joint Body Parsing & Pose Estimation Network and A New Benchmark](https://mlsanity.com/paper/1804.01984/) (2018)
- [Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2206.13028/) (2022)
- [Devil in the Details: Towards Accurate Single and Multiple Human Parsing](https://mlsanity.com/paper/1809.05996/) (2018)
- [VPN: Learning Video-Pose Embedding for Activities of Daily Living](https://mlsanity.com/paper/2007.03056/) (2020)
- [Deep Human Parsing with Active Template Regression](https://mlsanity.com/paper/1503.02391/) (2015)
- [Joint-bone Fusion Graph Convolutional Network for Semi-supervised Skeleton Action Recognition](https://mlsanity.com/paper/2202.04075/) (2022)
- [Interactive Spatiotemporal Token Attention Network for Skeleton-based General Interactive Action Recognition](https://mlsanity.com/paper/2307.07469/) (2023)
- [PSUMNet: Unified Modality Part Streams are All You Need for Efficient Pose-based Action Recognition](https://mlsanity.com/paper/2208.05775/) (2022)

## Built on by (most cited first, 2 of 2)

- [Are Spatial-Temporal Graph Convolution Networks for Human Action Recognition Over-Parameterized?](https://mlsanity.com/paper/2505.10679/) (2025)
- [HDBN: A Novel Hybrid Dual-branch Network for Robust Skeleton-based Action Recognition](https://mlsanity.com/paper/2404.15719/) (2024)

## Abstract (arXiv)

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 .