# Towards Accurate Cross-Domain In-Bed Human Pose Estimation

arXiv: https://arxiv.org/abs/2110.03578 · HTML: https://mlsanity.com/paper/2110.03578/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 21 references that are in the map.
- First posted on arXiv 7 Oct 2021.

## Builds on (most cited first, 15 of 15)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [Simple Baselines for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1804.06208/) (2018)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [Deep Learning-Based Human Pose Estimation: A Survey](https://mlsanity.com/paper/2012.13392/) (2020)
- [Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach](https://mlsanity.com/paper/1704.02447/) (2017)
- [Learning Feature Pyramids for Human Pose Estimation](https://mlsanity.com/paper/1708.01101/) (2017)
- [Unsupervised Multi-Target Domain Adaptation Through Knowledge Distillation](https://mlsanity.com/paper/2007.07077/) (2020)
- [Regressive Domain Adaptation for Unsupervised Keypoint Detection](https://mlsanity.com/paper/2103.06175/) (2021)
- [Generative Pseudo-label Refinement for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2001.02950/) (2020)
- [Seeing Under the Cover: A Physics Guided Learning Approach for In-Bed Pose Estimation](https://mlsanity.com/paper/1907.02161/) (2019)
- [Joint Progressive Knowledge Distillation and Unsupervised Domain Adaptation](https://mlsanity.com/paper/2005.07839/) (2020)
- [Simultaneously-Collected Multimodal Lying Pose Dataset: Towards In-Bed Human Pose Monitoring under Adverse Vision Conditions](https://mlsanity.com/paper/2008.08735/) (2020)

## Built on by (most cited first, 6 of 6)

- [Anatomy-guided domain adaptation for 3D in-bed human pose estimation](https://mlsanity.com/paper/2211.12193/) (2022)
- [Under the Cover Infant Pose Estimation using Multimodal Data](https://mlsanity.com/paper/2210.00662/) (2022)
- [Towards Simple and Accurate Human Pose Estimation with Stair Network](https://mlsanity.com/paper/2202.09115/) (2022)
- [In-Bed Pose Estimation: A Review](https://mlsanity.com/paper/2402.00700/) (2024)
- [Improving the Plausibility of Pressure Distributions Synthesized from Depth Image through Generative Modeling](https://mlsanity.com/paper/2512.13757/) (2025)
- [Geometry-Conditioned Diffusion for Occlusion-Robust In-Bed Pose Estimation](https://mlsanity.com/paper/2604.23651/) (2026)

## Abstract (arXiv)

Human behavioral monitoring during sleep is essential for various medical applications. Majority of the contactless human pose estimation algorithms are based on RGB modality, causing ineffectiveness in in-bed pose estimation due to occlusions by blankets and varying illumination conditions. Long-wavelength infrared (LWIR) modality based pose estimation algorithms overcome the aforementioned challenges; however, ground truth pose generations by a human annotator under such conditions are not feasible. A feasible solution to address this issue is to transfer the knowledge learned from images with pose labels and no occlusions, and adapt it towards real world conditions (occlusions due to blankets). In this paper, we propose a novel learning strategy comprises of two-fold data augmentation to reduce the cross-domain discrepancy and knowledge distillation to learn the distribution of unlabeled images in real world conditions. Our experiments and analysis show the effectiveness of our approach over multiple standard human pose estimation baselines.