# A Temporal Densely Connected Recurrent Network for Event-based Human Pose Estimation

arXiv: https://arxiv.org/abs/2209.07034 · HTML: https://mlsanity.com/paper/2209.07034/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 54 references that are in the map.
- First posted on arXiv 15 Sep 2022.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Simple Baselines for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1804.06208/) (2018)
- [Cascaded Pyramid Network for Multi-Person Pose Estimation](https://mlsanity.com/paper/1711.07319/) (2017)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation](https://mlsanity.com/paper/1908.10357/) (2019)
- [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](https://mlsanity.com/paper/1802.06898/) (2018)
- [HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification](https://mlsanity.com/paper/1803.07913/) (2018)
- [The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM](https://mlsanity.com/paper/1610.08336/) (2016)
- [Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars](https://mlsanity.com/paper/1804.01310/) (2018)
- [DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model](https://mlsanity.com/paper/1605.03170/) (2016)
- [PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation](https://mlsanity.com/paper/2105.02465/) (2021)
- [EventCap: Monocular 3D Capture of High-Speed Human Motions using an Event Camera](https://mlsanity.com/paper/1908.11505/) (2019)
- [Flowing ConvNets for Human Pose Estimation in Videos](https://mlsanity.com/paper/1506.02897/) (2015)
- [Detect-and-Track: Efficient Pose Estimation in Videos](https://mlsanity.com/paper/1712.09184/) (2017)
- [Deep Dual Consecutive Network for Human Pose Estimation](https://mlsanity.com/paper/2103.07254/) (2021)
- [HFirst: A Temporal Approach to Object Recognition](https://mlsanity.com/paper/1508.01176/) (2015)
- [UniPose: Unified Human Pose Estimation in Single Images and Videos](https://mlsanity.com/paper/2001.08095/) (2020)
- [PoseTrack: Joint Multi-Person Pose Estimation and Tracking](https://mlsanity.com/paper/1611.07727/) (2016)
- [EventHPE: Event-based 3D Human Pose and Shape Estimation](https://mlsanity.com/paper/2108.06819/) (2021)
- [Thin-Slicing Network: A Deep Structured Model for Pose Estimation in Videos](https://mlsanity.com/paper/1703.10898/) (2017)
- [Dynamic Kernel Distillation for Efficient Pose Estimation in Videos](https://mlsanity.com/paper/1908.09216/) (2019)
- [LSTM Pose Machines](https://mlsanity.com/paper/1712.06316/) (2017)
- [Relation-Based Associative Joint Location for Human Pose Estimation in Videos](https://mlsanity.com/paper/2107.03591/) (2021)

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

- [Exploring Event-based Human Pose Estimation with 3D Event Representations](https://mlsanity.com/paper/2311.04591/) (2023)
- [Hardware, Algorithms, and Applications of the Neuromorphic Vision Sensor: a Review](https://mlsanity.com/paper/2504.08588/) (2025)
- [Exploiting Spatiotemporal Properties for Efficient Event-Driven Human Pose Estimation](https://mlsanity.com/paper/2512.06306/) (2025)
- [ControlEvents: Controllable Synthesis of Event Camera Datawith Foundational Prior from Image Diffusion Models](https://mlsanity.com/paper/2509.22864/) (2025)
- [Semantic-E2VID: a Semantic-Enriched Paradigm for Event-to-Video Reconstruction](https://mlsanity.com/paper/2510.17347/) (2025)

## Abstract (arXiv)

Event camera is an emerging bio-inspired vision sensors that report per-pixel brightness changes asynchronously. It holds noticeable advantage of high dynamic range, high speed response, and low power budget that enable it to best capture local motions in uncontrolled environments. This motivates us to unlock the potential of event cameras for human pose estimation, as the human pose estimation with event cameras is rarely explored. Due to the novel paradigm shift from conventional frame-based cameras, however, event signals in a time interval contain very limited information, as event cameras can only capture the moving body parts and ignores those static body parts, resulting in some parts to be incomplete or even disappeared in the time interval. This paper proposes a novel densely connected recurrent architecture to address the problem of incomplete information. By this recurrent architecture, we can explicitly model not only the sequential but also non-sequential geometric consistency across time steps to accumulate information from previous frames to recover the entire human bodies, achieving a stable and accurate human pose estimation from event data. Moreover, to better evaluate our model, we collect a large scale multimodal event-based dataset that comes with human pose annotations, which is by far the most challenging one to the best of our knowledge. The experimental results on two public datasets and our own dataset demonstrate the effectiveness and strength of our approach. Code can be available online for facilitating the future research.