# Human Pose Estimation in Extremely Low-Light Conditions

arXiv: https://arxiv.org/abs/2303.15410 · HTML: https://mlsanity.com/paper/2303.15410/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 81 references that are in the map.
- First posted on arXiv 27 Mar 2023.

## Builds on (most cited first, 30 of 46)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/1811.12833/) (2018)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [Simple Baselines for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1804.06208/) (2018)
- [EnlightenGAN: Deep Light Enhancement without Paired Supervision](https://mlsanity.com/paper/1906.06972/) (2019)
- [Learning to See in the Dark](https://mlsanity.com/paper/1805.01934/) (2018)
- [Improving robustness against common corruptions by covariate shift adaptation](https://mlsanity.com/paper/2006.16971/) (2020)
- [Semantic Foggy Scene Understanding with Synthetic Data](https://mlsanity.com/paper/1708.07819/) (2017)
- [Cascaded Pyramid Network for Multi-Person Pose Estimation](https://mlsanity.com/paper/1711.07319/) (2017)
- [DeepPose: Human Pose Estimation via Deep Neural Networks](https://mlsanity.com/paper/1312.4659/) (2013)
- [Unifying distillation and privileged information](https://mlsanity.com/paper/1511.03643/) (2015)
- [LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement](https://mlsanity.com/paper/1511.03995/) (2015)
- [CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark](https://mlsanity.com/paper/1812.00324/) (2018)
- [Domain-Specific Batch Normalization for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1906.03950/) (2019)
- [Low-Light Image Enhancement with Normalizing Flow](https://mlsanity.com/paper/2109.05923/) (2021)
- [Getting to Know Low-light Images with The Exclusively Dark Dataset](https://mlsanity.com/paper/1805.11227/) (2018)
- [A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising](https://mlsanity.com/paper/2003.12751/) (2020)
- [Cross-Domain Adaptation for Animal Pose Estimation](https://mlsanity.com/paper/1908.05806/) (2019)
- [Pose2Seg: Detection Free Human Instance Segmentation](https://mlsanity.com/paper/1803.10683/) (2018)

## Built on by (most cited first, 27 of 27)

- [Towards Robust Event-guided Low-Light Image Enhancement: A Large-Scale Real-World Event-Image Dataset and Novel Approach](https://mlsanity.com/paper/2404.00834/) (2024)
- [Multi-Object Tracking in the Dark](https://mlsanity.com/paper/2405.06600/) (2024)
- [EvLight++: Low-Light Video Enhancement with an Event Camera: A Large-Scale Real-World Dataset, Novel Method, and More](https://mlsanity.com/paper/2408.16254/) (2024)
- [FREST: Feature RESToration for Semantic Segmentation under Multiple Adverse Conditions](https://mlsanity.com/paper/2407.13437/) (2024)
- [Domain-Adaptive 2D Human Pose Estimation via Dual Teachers in Extremely Low-Light Conditions](https://mlsanity.com/paper/2407.15451/) (2024)
- [Unsupervised Image Prior via Prompt Learning and CLIP Semantic Guidance for Low-Light Image Enhancement](https://mlsanity.com/paper/2405.11478/) (2024)
- [Improving Low-Light Image Recognition Performance Based on Image-adaptive Learnable Module](https://mlsanity.com/paper/2401.06438/) (2024)
- [Acoustic-based 3D Human Pose Estimation Robust to Human Position](https://mlsanity.com/paper/2411.07165/) (2024)
- [Leveraging Content and Context Cues for Low-Light Image Enhancement](https://mlsanity.com/paper/2412.07693/) (2024)
- [Event-Illumination Collaborative Low-light Image Enhancement with a High-resolution Real-world Dataset](https://mlsanity.com/paper/2605.22186/) (2026)
- [Towards Effective Collaborative Learning in Long-Tailed Recognition](https://mlsanity.com/paper/2305.03378/) (2023)
- [Troublemaker Learning for Low-Light Image Enhancement](https://mlsanity.com/paper/2402.04584/) (2024)
- [Depth-based Privileged Information for Boosting 3D Human Pose Estimation on RGB](https://mlsanity.com/paper/2409.11104/) (2024)
- [Robust Low-Light Human Pose Estimation through Illumination-Texture Modulation](https://mlsanity.com/paper/2501.08038/) (2025)
- [BGM2Pose: Active 3D Human Pose Estimation with Non-Stationary Sounds](https://mlsanity.com/paper/2503.00389/) (2025)
- [VST-Pose: A Velocity-Integrated Spatiotem-poral Attention Network for Human WiFi Pose Estimation](https://mlsanity.com/paper/2507.09672/) (2025)
- [ELVIS: Enhance Low-Light for Video Instance Segmentation in the Dark](https://mlsanity.com/paper/2512.01495/) (2025)
- [EgoXtreme: A Dataset for Robust Object Pose Estimation in Egocentric Views under Extreme Conditions](https://mlsanity.com/paper/2603.25135/) (2026)
- [UDAPose: Unsupervised Domain Adaptation for Low-Light Human Pose Estimation](https://mlsanity.com/paper/2604.10485/) (2026)
- [Cross-Domain Knowledge Distillation for Low-Resolution Human Pose Estimation](https://mlsanity.com/paper/2405.11448/) (2024)
- [Recognition-Oriented Low-Light Image Enhancement based on Global and Pixelwise Optimization](https://mlsanity.com/paper/2501.04210/) (2025)
- [DarkQA: Benchmarking Vision-Language Models on Visual-Primitive Question Answering in Low-Light Indoor Scenes](https://mlsanity.com/paper/2512.24985/) (2025)
- [LiDAR-Anchored Collaborative Distillation for Robust 2D Representations](https://mlsanity.com/paper/2602.12524/) (2026)
- [MERIT: Multi-domain Efficient RAW Image Translation](https://mlsanity.com/paper/2603.20836/) (2026)
- [Robust Promptable Video Object Segmentation](https://mlsanity.com/paper/2605.12006/) (2026)
- [LUMINA-26: Low-Light Understanding for Modeling and Interpreting Night-time Actions](https://mlsanity.com/paper/2606.23118/) (2026)
- [Sound-based Multi-Person 3D Pose Estimation](https://mlsanity.com/paper/2609.04902/) (2026)

## Abstract (arXiv)

We study human pose estimation in extremely low-light images. This task is challenging due to the difficulty of collecting real low-light images with accurate labels, and severely corrupted inputs that degrade prediction quality significantly. To address the first issue, we develop a dedicated camera system and build a new dataset of real low-light images with accurate pose labels. Thanks to our camera system, each low-light image in our dataset is coupled with an aligned well-lit image, which enables accurate pose labeling and is used as privileged information during training. We also propose a new model and a new training strategy that fully exploit the privileged information to learn representation insensitive to lighting conditions. Our method demonstrates outstanding performance on real extremely low light images, and extensive analyses validate that both of our model and dataset contribute to the success.