https://github.com/sananand007/Xrelab
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Papers with Code 아카이브
Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 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/sandman002/One-Style-is-All-You-Need-to-Generate-a-Video
https://github.com/sanghyun-son/EDSR-PyTorch
https://github.com/sanweiliti/PLACE
https://github.com/sap3d/Sap3D
https://github.com/satyaborg/pose-estimation-detection
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
https://github.com/sberbank-ai/DigiTales
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/sberbank-ai/DigiTeller
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/sberbank-ai/real-esrgan
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/sbetageri/MaskRCNN
https://github.com/scolocke/Traffic_Sign_ID_GTSRB_GTSDB
https://github.com/sczhou/upscale-a-video
Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution
https://github.com/sdauzcm/sr-basicsr
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/sdkchris/Exercise-Sheet-1
https://github.com/sdll/hrnet-pose-estimation
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/sdxone/resolution
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/seanmcgovern21/Machine-Learning-CS539
https://github.com/securade/hub
https://github.com/see--/keras-centernet
https://github.com/senguptaumd/SfSNet
An All-In-One Convolutional Neural Network for Face Analysis
https://github.com/sense-x/uniformer
UniFormer: Unifying Convolution and Self-attention for Visual Recognition
https://github.com/seominseok0429/Learning-Loss-for-Active-Learning-Pytorch
https://github.com/seominseok0429/inception-I3D-NON-LOCAL
https://github.com/serviceberry3/3d-pose-baseline_android
A simple yet effective baseline for 3d human pose estimation
https://github.com/sgoldyaev/DeepFashion.ADGAN
https://github.com/sgrvinod/a-pytorch-tutorial-to-super-resolution
https://github.com/shamangary/FSA-Net
https://github.com/shanglianlm0525/CvPytorch
https://github.com/shayeree96/Adversial-Attacks-on-densely-fused-point-clouds-for-6D-Pose-Estimation
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
https://github.com/shichaoy/cuboid_detect
https://github.com/shichaoy/matlab_cuboid_detect
https://github.com/shihenw/convolutional-pose-machines-release
https://github.com/shijianjian/HRNet_Keras
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/shinseung428/simGAN_NYU_Hand
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/shiqiyu/libfacedetection.train
https://github.com/shkarupa-alex/tfvan
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.