# Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation

arXiv: https://arxiv.org/abs/2302.01593 · HTML: https://mlsanity.com/paper/2302.01593/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 43 references that are in the map.
- First posted on arXiv 3 Feb 2023.

## Builds on (most cited first, 30 of 33)

- [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)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://mlsanity.com/paper/2201.12329/) (2022)
- [Simple Baselines for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1804.06208/) (2018)
- [DN-DETR: Accelerate DETR Training by Introducing Query DeNoising](https://mlsanity.com/paper/2203.01305/) (2022)
- [Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation](https://mlsanity.com/paper/2206.02777/) (2022)
- [HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation](https://mlsanity.com/paper/1908.10357/) (2019)
- [CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark](https://mlsanity.com/paper/1812.00324/) (2018)
- [Associative Embedding: End-to-End Learning for Joint Detection and Grouping](https://mlsanity.com/paper/1611.05424/) (2016)
- [Human Pose Regression with Residual Log-likelihood Estimation](https://mlsanity.com/paper/2107.11291/) (2021)
- [Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression](https://mlsanity.com/paper/2104.02300/) (2021)
- [Pose Recognition with Cascade Transformers](https://mlsanity.com/paper/2104.06976/) (2021)
- [Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation](https://mlsanity.com/paper/2012.15175/) (2020)
- [DenseBox: Unifying Landmark Localization with End to End Object Detection](https://mlsanity.com/paper/1509.04874/) (2015)
- [DirectPose: Direct End-to-End Multi-Person Pose Estimation](https://mlsanity.com/paper/1911.07451/) (2019)
- [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)
- [FCPose: Fully Convolutional Multi-Person Pose Estimation with Dynamic Instance-Aware Convolutions](https://mlsanity.com/paper/2105.14185/) (2021)

## Built on by (most cited first, 30 of 48)

- [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://mlsanity.com/paper/2401.14159/) (2024)
- [Effective Whole-body Pose Estimation with Two-stages Distillation](https://mlsanity.com/paper/2307.15880/) (2023)
- [Motion-X: A Large-scale 3D Expressive Whole-body Human Motion Dataset](https://mlsanity.com/paper/2307.00818/) (2023)
- [VisionLLM v2: An End-to-End Generalist Multimodal Large Language Model for Hundreds of Vision-Language Tasks](https://mlsanity.com/paper/2406.08394/) (2024)
- [HumanSD: A Native Skeleton-Guided Diffusion Model for Human Image Generation](https://mlsanity.com/paper/2304.04269/) (2023)
- [HumanTOMATO: Text-aligned Whole-body Motion Generation](https://mlsanity.com/paper/2310.12978/) (2023)
- [DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding](https://mlsanity.com/paper/2411.14347/) (2024)
- [Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes](https://mlsanity.com/paper/2303.02760/) (2023)
- [AiOS: All-in-One-Stage Expressive Human Pose and Shape Estimation](https://mlsanity.com/paper/2403.17934/) (2024)
- [SkeletonMAE: Graph-based Masked Autoencoder for Skeleton Sequence Pre-training](https://mlsanity.com/paper/2307.08476/) (2023)
- [RTMO: Towards High-Performance One-Stage Real-Time Multi-Person Pose Estimation](https://mlsanity.com/paper/2312.07526/) (2023)
- [Group Pose: A Simple Baseline for End-to-End Multi-person Pose Estimation](https://mlsanity.com/paper/2308.07313/) (2023)
- [Hulk: A Universal Knowledge Translator for Human-Centric Tasks](https://mlsanity.com/paper/2312.01697/) (2023)
- [X-Pose: Detecting Any Keypoints](https://mlsanity.com/paper/2310.08530/) (2023)
- [SAT-HMR: Real-Time Multi-Person 3D Mesh Estimation via Scale-Adaptive Tokens](https://mlsanity.com/paper/2411.19824/) (2024)
- [Neural Interactive Keypoint Detection](https://mlsanity.com/paper/2308.10174/) (2023)
- [MoCapAnything: Unified 3D Motion Capture for Arbitrary Skeletons from Monocular Videos](https://mlsanity.com/paper/2512.10881/) (2025)
- [LPFormer: LiDAR Pose Estimation Transformer with Multi-Task Network](https://mlsanity.com/paper/2306.12525/) (2023)
- [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)
- [HandOS: 3D Hand Reconstruction in One Stage](https://mlsanity.com/paper/2412.01537/) (2024)
- [detrex: Benchmarking Detection Transformers](https://mlsanity.com/paper/2306.07265/) (2023)
- [H-MoRe: Learning Human-centric Motion Representation for Action Analysis](https://mlsanity.com/paper/2504.10676/) (2025)
- [Human Pose-based Estimation, Tracking and Action Recognition with Deep Learning: A Survey](https://mlsanity.com/paper/2310.13039/) (2023)
- [Referring Human Pose and Mask Estimation in the Wild](https://mlsanity.com/paper/2410.20508/) (2024)
- [HumanMM: Global Human Motion Recovery from Multi-shot Videos](https://mlsanity.com/paper/2503.07597/) (2025)
- [Learning Structure-Guided Diffusion Model for 2D Human Pose Estimation](https://mlsanity.com/paper/2306.17074/) (2023)
- [KptLLM: Unveiling the Power of Large Language Model for Keypoint Comprehension](https://mlsanity.com/paper/2411.01846/) (2024)
- [EgoPoseFormer: A Simple Baseline for Stereo Egocentric 3D Human Pose Estimation](https://mlsanity.com/paper/2403.18080/) (2024)
- [EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation](https://mlsanity.com/paper/2603.18739/) (2026)
- [BPJDet: Extended Object Representation for Generic Body-Part Joint Detection](https://mlsanity.com/paper/2304.10765/) (2023)

## Abstract (arXiv)

This paper presents a novel end-to-end framework with Explicit box Detection for multi-person Pose estimation, called ED-Pose, where it unifies the contextual learning between human-level (global) and keypoint-level (local) information. Different from previous one-stage methods, ED-Pose re-considers this task as two explicit box detection processes with a unified representation and regression supervision. First, we introduce a human detection decoder from encoded tokens to extract global features. It can provide a good initialization for the latter keypoint detection, making the training process converge fast. Second, to bring in contextual information near keypoints, we regard pose estimation as a keypoint box detection problem to learn both box positions and contents for each keypoint. A human-to-keypoint detection decoder adopts an interactive learning strategy between human and keypoint features to further enhance global and local feature aggregation. In general, ED-Pose is conceptually simple without post-processing and dense heatmap supervision. It demonstrates its effectiveness and efficiency compared with both two-stage and one-stage methods. Notably, explicit box detection boosts the pose estimation performance by 4.5 AP on COCO and 9.9 AP on CrowdPose. For the first time, as a fully end-to-end framework with a L1 regression loss, ED-Pose surpasses heatmap-based Top-down methods under the same backbone by 1.2 AP on COCO and achieves the state-of-the-art with 76.6 AP on CrowdPose without bells and whistles. Code is available at https://github.com/IDEA-Research/ED-Pose.