Browse State-of-the-Art › 2D Human Pose Estimation

2D Human Pose Estimation

68 papers with code · 10 benchmarks · 25 datasets archive 2025-07-28

Computer VisionKnowledge BaseReasoning

What is Human Pose Estimation? Human pose estimation is the process of estimating the configuration of the body (pose) from a single, typically monocular, image. Background. Human pose estimation is one of the key problems in computer vision that has been studied for well over 15 years. The reason for its importance is the abundance of applications that can benefit from such a technology. For example, human pose estimation allows for higher-level reasoning in the context of human-computer interaction and activity recognition; it is also one of the basic building blocks for marker-less motion capture (MoCap) technology. MoCap technology is useful for applications ranging from character animation to clinical analysis of gait pathologies.

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

10 leaderboard tables shown for this task, 10 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

DatasetBest model (first row in archive order)PaperCodeSyntologyCompare
COCO-WholeBody (15 rows) RTMW-x RTMW: Real-Time Multi-Person 2D and 3D Whole-body Pose Estimation code — Compare
OCHuman (11 rows) BBox-Mask-Pose 2x Detection, Pose Estimation and Segmentation for Multiple Bodies:... code — Compare
Human-Art (10 rows) UniPose X-Pose: Detecting Any Keypoints code Syntology ran 11 of 13 samples · 2 unverified Compare
JHMDB (2D poses only) (5 rows) DeciWatch DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation code Syntology ran 1 of 8 samples · 7 unverified Compare
Alibaba Cluster Trace (1 row) mitsimpo Alibaba at IJCNLP-2017 Task 1: Embedding Grammatical Features into... — — Compare
ExLPose-LL-E (1 row) DA-LLPose Domain-Adaptive 2D Human Pose Estimation via Dual Teachers in... code Syntology ran 11 of 13 samples · 2 unverified Compare
ExLPose-LL-H (1 row) DA-LLPose Domain-Adaptive 2D Human Pose Estimation via Dual Teachers in... code Syntology ran 11 of 13 samples · 2 unverified Compare
ExLPose-LL-N (1 row) DA-LLPose Domain-Adaptive 2D Human Pose Estimation via Dual Teachers in... code Syntology ran 11 of 13 samples · 2 unverified Compare
ExLPose-OCN-RICOH3 (1 row) DA-LLPose Domain-Adaptive 2D Human Pose Estimation via Dual Teachers in... code Syntology ran 11 of 13 samples · 2 unverified Compare
ExLPose-OCN-A7M3 (1 row) DA-LLPose Domain-Adaptive 2D Human Pose Estimation via Dual Teachers in... code Syntology ran 11 of 13 samples · 2 unverified Compare

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

25 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

6 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 68 papers with code (118 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

Syntology lines on 16 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.

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