# Human Pose Estimation using Deep Consensus Voting

arXiv: https://arxiv.org/abs/1603.08212 · HTML: https://mlsanity.com/paper/1603.08212/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2016.

## Built on by (most cited first, 14 of 14)

- [Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression](https://mlsanity.com/paper/2104.02300/) (2021)
- [TokenPose: Learning Keypoint Tokens for Human Pose Estimation](https://mlsanity.com/paper/2104.03516/) (2021)
- [Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective](https://mlsanity.com/paper/2104.11536/) (2021)
- [Human Pose as Compositional Tokens](https://mlsanity.com/paper/2303.11638/) (2023)
- [TFPose: Direct Human Pose Estimation with Transformers](https://mlsanity.com/paper/2103.15320/) (2021)
- [An Adversarial Human Pose Estimation Network Injected with Graph Structure](https://mlsanity.com/paper/2103.15534/) (2021)
- [Human Pose-based Estimation, Tracking and Action Recognition with Deep Learning: A Survey](https://mlsanity.com/paper/2310.13039/) (2023)
- [Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models](https://mlsanity.com/paper/2305.13765/) (2023)
- [Single Person Pose Estimation: A Survey](https://mlsanity.com/paper/2109.10056/) (2021)
- [Collaborative Multi-Agent Video Fast-Forwarding](https://mlsanity.com/paper/2305.17569/) (2023)
- [Bias-Compensated Integral Regression for Human Pose Estimation](https://mlsanity.com/paper/2301.10431/) (2023)
- [HoughNet: Integrating near and long-range evidence for visual detection](https://mlsanity.com/paper/2104.06773/) (2021)
- [FasterPose: A Faster Simple Baseline for Human Pose Estimation](https://mlsanity.com/paper/2107.03215/) (2021)
- [Self-Supervision and Spatial-Sequential Attention Based Loss for Multi-Person Pose Estimation](https://mlsanity.com/paper/2110.10734/) (2021)

## Abstract (arXiv)

In this paper we consider the problem of human pose estimation from a single still image. We propose a novel approach where each location in the image votes for the position of each keypoint using a convolutional neural net. The voting scheme allows us to utilize information from the whole image, rather than rely on a sparse set of keypoint locations. Using dense, multi-target votes, not only produces good keypoint predictions, but also enables us to compute image-dependent joint keypoint probabilities by looking at consensus voting. This differs from most previous methods where joint probabilities are learned from relative keypoint locations and are independent of the image. We finally combine the keypoints votes and joint probabilities in order to identify the optimal pose configuration. We show our competitive performance on the MPII Human Pose and Leeds Sports Pose datasets.