# How Robust is 3D Human Pose Estimation to Occlusion?

arXiv: https://arxiv.org/abs/1808.09316 · HTML: https://mlsanity.com/paper/1808.09316/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2018.

## Built on by (most cited first, 23 of 23)

- [PARE: Part Attention Regressor for 3D Human Body Estimation](https://mlsanity.com/paper/2104.08527/) (2021)
- [CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation](https://mlsanity.com/paper/2208.00571/) (2022)
- [HandOccNet: Occlusion-Robust 3D Hand Mesh Estimation Network](https://mlsanity.com/paper/2203.14564/) (2022)
- [Body Meshes as Points](https://mlsanity.com/paper/2105.02467/) (2021)
- [3D-Aware Neural Body Fitting for Occlusion Robust 3D Human Pose Estimation](https://mlsanity.com/paper/2308.10123/) (2023)
- [Occluded Human Body Capture with Self-Supervised Spatial-Temporal Motion Prior](https://mlsanity.com/paper/2207.05375/) (2022)
- [Rendering Humans from Object-Occluded Monocular Videos](https://mlsanity.com/paper/2308.04622/) (2023)
- [Domain Adaptive 3D Pose Augmentation for In-the-wild Human Mesh Recovery](https://mlsanity.com/paper/2206.10457/) (2022)
- [The Best of Both Worlds: Combining Model-based and Nonparametric Approaches for 3D Human Body Estimation](https://mlsanity.com/paper/2205.00508/) (2022)
- [Multi-view Pose Fusion for Occlusion-Aware 3D Human Pose Estimation](https://mlsanity.com/paper/2408.15810/) (2024)
- [Improving the Robustness of 3D Human Pose Estimation: A Benchmark and Learning from Noisy Input](https://mlsanity.com/paper/2312.06797/) (2023)
- [HandGCAT: Occlusion-Robust 3D Hand Mesh Reconstruction from Monocular Images](https://mlsanity.com/paper/2403.07912/) (2024)
- [Benchmarking 3D Human Pose Estimation Models under Occlusions](https://mlsanity.com/paper/2504.10350/) (2025)
- [Real-Time Human Pose Estimation on a Smart Walker using Convolutional Neural Networks](https://mlsanity.com/paper/2106.14739/) (2021)
- [PONet: Robust 3D Human Pose Estimation via Learning Orientations Only](https://mlsanity.com/paper/2112.11153/) (2021)
- [Live Stream Temporally Embedded 3D Human Body Pose and Shape Estimation](https://mlsanity.com/paper/2207.12537/) (2022)
- [Category-Level Pose Retrieval with Contrastive Features Learnt with Occlusion Augmentation](https://mlsanity.com/paper/2208.06195/) (2022)
- [Geometry-Biased Transformer for Robust Multi-View 3D Human Pose Reconstruction](https://mlsanity.com/paper/2312.17106/) (2023)
- [3D Human Pose Estimation with Occlusions: Introducing BlendMimic3D Dataset and GCN Refinement](https://mlsanity.com/paper/2404.16136/) (2024)
- [Di2Pose: Discrete Diffusion Model for Occluded 3D Human Pose Estimation](https://mlsanity.com/paper/2405.17016/) (2024)
- [Error Estimation for Single-Image Human Body Mesh Reconstruction](https://mlsanity.com/paper/2305.17245/) (2023)
- [DeProPose: Deficiency-Proof 3D Human Pose Estimation via Adaptive Multi-View Fusion](https://mlsanity.com/paper/2502.16419/) (2025)
- [RealOOB: A Definition-Consistent Real-World Oriented Occlusion Boundary Benchmark](https://mlsanity.com/paper/2608.30820/) (2026)

## Abstract (arXiv)

Occlusion is commonplace in realistic human-robot shared environments, yet its effects are not considered in standard 3D human pose estimation benchmarks. This leaves the question open: how robust are state-of-the-art 3D pose estimation methods against partial occlusions? We study several types of synthetic occlusions over the Human3.6M dataset and find a method with state-of-the-art benchmark performance to be sensitive even to low amounts of occlusion. Addressing this issue is key to progress in applications such as collaborative and service robotics. We take a first step in this direction by improving occlusion-robustness through training data augmentation with synthetic occlusions. This also turns out to be an effective regularizer that is beneficial even for non-occluded test cases.