SOTAVerified

Keypoint Detection

Keypoint Detection is essential for analyzing and interpreting images in computer vision. It involves simultaneously detecting and localizing interesting points in an image. Keypoints, also known as interest points, are spatial locations or points in the image that define what is interesting or what stands out. They are invariant to image rotation, shrinkage, translation, distortion, etc. Keypoints examples are body joints, facial landmarks, or any other salient points in objects. Keypoints have uses in problems such as pose estimation, object detection and tracking, facial analysis, and augmented reality.

( Image credit: PifPaf: Composite Fields for Human Pose Estimation; "Learning to surf" by fotologic, license: CC-BY-2.0 )

Papers

Showing 110 of 339 papers

TitleStatusHype
Sapiens: Foundation for Human Vision ModelsCode9
Images Speak in Images: A Generalist Painter for In-Context Visual LearningCode4
RMPE: Regional Multi-person Pose EstimationCode3
ViTPose: Simple Vision Transformer Baselines for Human Pose EstimationCode3
Realtime Multi-Person 2D Pose Estimation using Part Affinity FieldsCode3
ViTPose++: Vision Transformer for Generic Body Pose EstimationCode3
Objects as PointsCode2
InstructDiffusion: A Generalist Modeling Interface for Vision TasksCode2
OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal AssociationCode2
GSplatLoc: Grounding Keypoint Descriptors into 3D Gaussian Splatting for Improved Visual LocalizationCode2
Show:102550
← PrevPage 1 of 34Next →

No leaderboard results yet.