Papers with Code 아카이브
Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 31
- 보존된 평가 기록
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Pose Estimation is a computer vision task where the goal is to detect the position and orientation of a person or an object. Usually, this is done by predicting the location of specific keypoints like hands, head, elbows, etc. in case of Human Pose Estimation.
A common benchmark for this task is MPII Human Pose
<span style="color:grey; opacity: 0.6">( Image credit: Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Anchoret13/blenderset
https://github.com/AniketVashishtha/SUPER_RESOLUTION_DL
https://github.com/AnjanaGJoseph/Super-Resolution-GAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/Aprilkaka/nonlocal_mindspore
https://github.com/ArashHosseini/hand-graph-cnn-gpu
https://github.com/Araxeus/PNG-Upscale
https://github.com/Arnav0400/Headpose_Estimation
Nose, eyes and ears: Head pose estimation by locating facial keypoints
https://github.com/Arthur-Shi/py-faster-rcnn
https://github.com/Arthur151/CenterHMR
TRACE: 5D Temporal Regression of Avatars with Dynamic Cameras in 3D Environments
https://github.com/AshishSingh2261/Pedestrian_Instance_Segmentation
https://github.com/Asthestarsfalll/UniFormer-MegEngine
UniFormer: Unifying Convolution and Self-attention for Visual Recognition
https://github.com/Asthestarsfalll/VAN-MegEngine
https://github.com/Automunge/AutoMunge
https://github.com/AwaleSajil/SuperGlue_Demo_Modified1
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/Ayaanesmail/Test.-
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
https://github.com/B1ueber2y/NDiv
https://github.com/BB-Repos/BBpose
https://github.com/BIOINSu/LGFN
https://github.com/Baldins/Learning_UAV_Pose_Estimation
Learning Pose Estimation for UAV Autonomous Navigation andLanding Using Visual-Inertial Sensor Data
https://github.com/Barak123748596/CVDL_course_project
https://github.com/BearNinja123/esrGAN_vBearNinja123
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/Biantian/MscProject
https://github.com/BingkAI-B21CAP0161/C-Mask-Machine-Learning
https://github.com/BlackAngel1111/Fast-RCNN
https://github.com/BruceNolascoFC/RN-2021-1
https://github.com/BupyeongHealer/Mask_RCNN_tf_2.x
https://github.com/Burf/HRNetV2-OCR-Tensorflow2
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/Burf/tfdetection
https://github.com/CASIA-IVA-Lab/ISP-reID
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/CMU-Perceptual-Computing-Lab/MonocularTotalCapture
Monocular Total Capture: Posing Face, Body, and Hands in the Wild
https://github.com/CMU-Perceptual-Computing-Lab/openpose_caffe_train
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/CMU-Perceptual-Computing-Lab/openpose_unity_plugin
https://github.com/CSAILVision/semantic-segmentation-pytorch
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/Calylyli/Mindsporehrnet
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/CarstenIsert/DeepBurn
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.