https://github.com/HuangJunJie2017/UDP-Pose
Learning Delicate Local Representations for Multi-Person Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
Learning Delicate Local Representations for Multi-Person Pose Estimation
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark
Deep Alignment Network: A convolutional neural network for robust face alignment
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Hand Keypoint Detection in Single Images using Multiview Bootstrapping
Distribution-Aware Coordinate Representation for Human Pose Estimation
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Multi-Person Pose Estimation with Local Joint-to-Person Associations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.