# SPCNet:Spatial Preserve and Content-aware Network for Human Pose Estimation

arXiv: https://arxiv.org/abs/2004.05834 · HTML: https://mlsanity.com/paper/2004.05834/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [AdaptivePose: Human Parts as Adaptive Points](https://mlsanity.com/paper/2112.13635/) (2021)
- [Learning Quality-aware Representation for Multi-person Pose Regression](https://mlsanity.com/paper/2201.01087/) (2022)
- [AdaptivePose++: A Powerful Single-Stage Network for Multi-Person Pose Regression](https://mlsanity.com/paper/2210.04014/) (2022)
- [Geometric Pooling: maintaining more useful information](https://mlsanity.com/paper/2306.12341/) (2023)
- [Spatial Attention-based Distribution Integration Network for Human Pose Estimation](https://mlsanity.com/paper/2311.05323/) (2023)

## Abstract (arXiv)

Human pose estimation is a fundamental yet challenging task in computer vision. Although deep learning techniques have made great progress in this area, difficult scenarios (e.g., invisible keypoints, occlusions, complex multi-person scenarios, and abnormal poses) are still not well-handled. To alleviate these issues, we propose a novel Spatial Preserve and Content-aware Network(SPCNet), which includes two effective modules: Dilated Hourglass Module(DHM) and Selective Information Module(SIM). By using the Dilated Hourglass Module, we can preserve the spatial resolution along with large receptive field. Similar to Hourglass Network, we stack the DHMs to get the multi-stage and multi-scale information. Then, a Selective Information Module is designed to select relatively important features from different levels under a sufficient consideration of spatial content-aware mechanism and thus considerably improves the performance. Extensive experiments on MPII, LSP and FLIC human pose estimation benchmarks demonstrate the effectiveness of our network. In particular, we exceed previous methods and achieve the state-of-the-art performance on three aforementioned benchmark datasets.