https://github.com/shubhajitbasak/dense3dfacelandmarks
공식 코드A lightweight 3D dense facial landmark estimation model from position map data
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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.
<span style="color:grey; opacity: 0.6">( Image credit: PifPaf: Composite Fields for Human Pose Estimation; "Learning to surf" by fotologic, license: CC-BY-2.0 )</span>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
A lightweight 3D dense facial landmark estimation model from position map data
GoodPoint: unsupervised learning of keypoint detection and description
Self-supervised Learning of Contextualized Local Visual Embeddings
AggPose: Deep Aggregation Vision Transformer for Infant Pose Estimation
Regressive Domain Adaptation for Unsupervised Keypoint Detection
Semi-supervised Human Pose Estimation in Art-historical Images
Learning Keypoints from Synthetic Data for Robotic Cloth Folding
Centroid Distance Keypoint Detector for Colored Point Clouds
NerVE: Neural Volumetric Edges for Parametric Curve Extraction from Point Cloud
Learning Human-Object Interaction Detection using Interaction Points
Improving the matching of deformable objects by learning to detect keypoints
Learning to Detect Good Keypoints to Match Non-Rigid Objects in RGB Images
Center Direction Network for Grasping Point Localization on Cloths
Multi-Grained Contrast for Data-Efficient Unsupervised Representation Learning
OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association
OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association
ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation
NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same Action
Back to 3D: Few-Shot 3D Keypoint Detection with Back-Projected 2D Features
DeepDarts: Modeling Keypoints as Objects for Automatic Scorekeeping in Darts using a Single Camera
StarMap for Category-Agnostic Keypoint and Viewpoint Estimation
2.5D U-Net with Depth Reduction for 3D CryoET Object Identification
Open-Vocabulary Animal Keypoint Detection with Semantic-feature Matching
Learning Transferable Parameters for Unsupervised Domain Adaptation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.