# Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation

arXiv: https://arxiv.org/abs/2012.15175 · HTML: https://mlsanity.com/paper/2012.15175/

## Key facts

- Cited by 69 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2020.

## Built on by (most cited first, 30 of 69)

- [RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose](https://mlsanity.com/paper/2303.07399/) (2023)
- [DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding](https://mlsanity.com/paper/2411.14347/) (2024)
- [SimCC: a Simple Coordinate Classification Perspective for Human Pose Estimation](https://mlsanity.com/paper/2107.03332/) (2021)
- [Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation](https://mlsanity.com/paper/2302.01593/) (2023)
- [Human Pose as Compositional Tokens](https://mlsanity.com/paper/2303.11638/) (2023)
- [Poseur: Direct Human Pose Regression with Transformers](https://mlsanity.com/paper/2201.07412/) (2022)
- [TFPose: Direct Human Pose Estimation with Transformers](https://mlsanity.com/paper/2103.15320/) (2021)
- [Polarized Self-Attention: Towards High-quality Pixel-wise Regression](https://mlsanity.com/paper/2107.00782/) (2021)
- [RTMO: Towards High-Performance One-Stage Real-Time Multi-Person Pose Estimation](https://mlsanity.com/paper/2312.07526/) (2023)
- [Few-shot Keypoint Detection with Uncertainty Learning for Unseen Species](https://mlsanity.com/paper/2112.06183/) (2021)
- [Distribution-Aligned Diffusion for Human Mesh Recovery](https://mlsanity.com/paper/2308.13369/) (2023)
- [Group Pose: A Simple Baseline for End-to-End Multi-person Pose Estimation](https://mlsanity.com/paper/2308.07313/) (2023)
- [MolGrapher: Graph-based Visual Recognition of Chemical Structures](https://mlsanity.com/paper/2308.12234/) (2023)
- [2D Human Pose Estimation: A Survey](https://mlsanity.com/paper/2204.07370/) (2022)
- [Rethinking Keypoint Representations: Modeling Keypoints and Poses as Objects for Multi-Person Human Pose Estimation](https://mlsanity.com/paper/2111.08557/) (2021)
- [Learnable human mesh triangulation for 3D human pose and shape estimation](https://mlsanity.com/paper/2208.11251/) (2022)
- [GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing](https://mlsanity.com/paper/2412.02366/) (2024)
- [Dual networks based 3D Multi-Person Pose Estimation from Monocular Video](https://mlsanity.com/paper/2205.00748/) (2022)
- [QueryPose: Sparse Multi-Person Pose Regression via Spatial-Aware Part-Level Query](https://mlsanity.com/paper/2212.07855/) (2022)
- [Neural Interactive Keypoint Detection](https://mlsanity.com/paper/2308.10174/) (2023)
- [Video-Based Human Pose Regression via Decoupled Space-Time Aggregation](https://mlsanity.com/paper/2403.19926/) (2024)
- [Heatmap Distribution Matching for Human Pose Estimation](https://mlsanity.com/paper/2210.00740/) (2022)
- [You Only Learn One Query: Learning Unified Human Query for Single-Stage Multi-Person Multi-Task Human-Centric Perception](https://mlsanity.com/paper/2312.05525/) (2023)
- [Relation-Based Associative Joint Location for Human Pose Estimation in Videos](https://mlsanity.com/paper/2107.03591/) (2021)
- [Self-Correctable and Adaptable Inference for Generalizable Human Pose Estimation](https://mlsanity.com/paper/2303.11180/) (2023)
- [Meta-Point Learning and Refining for Category-Agnostic Pose Estimation](https://mlsanity.com/paper/2403.13647/) (2024)
- [AdaptivePose: Human Parts as Adaptive Points](https://mlsanity.com/paper/2112.13635/) (2021)
- [Learning Quality-aware Representation for Multi-person Pose Regression](https://mlsanity.com/paper/2201.01087/) (2022)
- [AdaptivePose++: A Powerful Single-Stage Network for Multi-Person Pose Regression](https://mlsanity.com/paper/2210.04014/) (2022)
- [Topology Repairing of Disconnected Pulmonary Airways and Vessels: Baselines and a Dataset](https://mlsanity.com/paper/2306.07089/) (2023)

## Abstract (arXiv)

Heatmap regression has become the most prevalent choice for nowadays human pose estimation methods. The ground-truth heatmaps are usually constructed via covering all skeletal keypoints by 2D gaussian kernels. The standard deviations of these kernels are fixed. However, for bottom-up methods, which need to handle a large variance of human scales and labeling ambiguities, the current practice seems unreasonable. To better cope with these problems, we propose the scale-adaptive heatmap regression (SAHR) method, which can adaptively adjust the standard deviation for each keypoint. In this way, SAHR is more tolerant of various human scales and labeling ambiguities. However, SAHR may aggravate the imbalance between fore-background samples, which potentially hurts the improvement of SAHR. Thus, we further introduce the weight-adaptive heatmap regression (WAHR) to help balance the fore-background samples. Extensive experiments show that SAHR together with WAHR largely improves the accuracy of bottom-up human pose estimation. As a result, we finally outperform the state-of-the-art model by +1.5AP and achieve 72.0AP on COCO test-dev2017, which is com-arable with the performances of most top-down methods. Source codes are available at https://github.com/greatlog/SWAHR-HumanPose.