# Human Pose Estimation on Privacy-Preserving Low-Resolution Depth Images

arXiv: https://arxiv.org/abs/2007.08340 · HTML: https://mlsanity.com/paper/2007.08340/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2020.

## Built on by (most cited first, 16 of 16)

- [SPAct: Self-supervised Privacy Preservation for Action Recognition](https://mlsanity.com/paper/2203.15205/) (2022)
- [STPrivacy: Spatio-Temporal Privacy-Preserving Action Recognition](https://mlsanity.com/paper/2301.03046/) (2023)
- [Privacy-Preserving Action Recognition via Motion Difference Quantization](https://mlsanity.com/paper/2208.02459/) (2022)
- [Anatomy-guided domain adaptation for 3D in-bed human pose estimation](https://mlsanity.com/paper/2211.12193/) (2022)
- [Unsupervised domain adaptation for clinician pose estimation and instance segmentation in the operating room](https://mlsanity.com/paper/2108.11801/) (2021)
- [Multi-Modal Unsupervised Pre-Training for Surgical Operating Room Workflow Analysis](https://mlsanity.com/paper/2207.07894/) (2022)
- [ST(OR)2: Spatio-Temporal Object Level Reasoning for Activity Recognition in the Operating Room](https://mlsanity.com/paper/2312.12250/) (2023)
- [Privacy-preserving Optics for Enhancing Protection in Face De-identification](https://mlsanity.com/paper/2404.00777/) (2024)
- [Spatial-temporal Hierarchical Reinforcement Learning for Interpretable Pathology Image Super-Resolution](https://mlsanity.com/paper/2406.18310/) (2024)
- [Balancing Privacy and Action Performance: A Penalty-Driven Approach to Image Anonymization](https://mlsanity.com/paper/2504.14301/) (2025)
- [Activity Recognition on Avatar-Anonymized Datasets with Masked Differential Privacy](https://mlsanity.com/paper/2410.17098/) (2024)
- [Recoverable Anonymization for Pose Estimation: A Privacy-Enhancing Approach](https://mlsanity.com/paper/2409.02715/) (2024)
- [StegaVAR: Privacy-Preserving Video Action Recognition via Steganographic Domain Analysis](https://mlsanity.com/paper/2512.12586/) (2025)
- [Self-supervised Learning via Cluster Distance Prediction for Operating Room Context Awareness](https://mlsanity.com/paper/2407.05448/) (2024)
- [Automated Patient Positioning with Learned 3D Hand Gestures](https://mlsanity.com/paper/2407.14903/) (2024)
- [Multi-modal On-Device Learning for Monocular Depth Estimation on Ultra-low-power MCUs](https://mlsanity.com/paper/2512.00086/) (2025)

## Abstract (arXiv)

Human pose estimation (HPE) is a key building block for developing AI-based context-aware systems inside the operating room (OR). The 24/7 use of images coming from cameras mounted on the OR ceiling can however raise concerns for privacy, even in the case of depth images captured by RGB-D sensors. Being able to solely use low-resolution privacy-preserving images would address these concerns and help scale up the computer-assisted approaches that rely on such data to a larger number of ORs. In this paper, we introduce the problem of HPE on low-resolution depth images and propose an end-to-end solution that integrates a multi-scale super-resolution network with a 2D human pose estimation network. By exploiting intermediate feature-maps generated at different super-resolution, our approach achieves body pose results on low-resolution images (of size 64x48) that are on par with those of an approach trained and tested on full resolution images (of size 640x480).