https://github.com/xuxy09/texformer
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning
Papers with Code 아카이브
Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 17
- 평가표
- 31
- 보존된 평가 기록
- 235
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/xwinxu/bayesde
https://github.com/xwinxu/bayesian-sde
https://github.com/xxlong0/wonder3d
https://github.com/xyjbaal/FPCC
Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Picking
https://github.com/xzabg/faster-rcnn-with-Caltech
https://github.com/xzr12/PredCNN
https://github.com/yakhyo/head-pose-estimation
https://github.com/yangdashi888/CenterNet-windows
https://github.com/yangsenius/Pose-Neural-Fabrics-Search
https://github.com/yangyi02/3d_pose_fine_grained
https://github.com/yangyi02/finegrained-pose
https://github.com/yangyucheng000/AlphaPose
https://github.com/yangyucheng000/ArtTrack
https://github.com/yangyucheng000/ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/yangyucheng000/HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/yangyucheng000/LiteHRNet
https://github.com/yangyucheng000/MSPN
Rethinking on Multi-Stage Networks for Human Pose Estimation
https://github.com/yangyucheng000/Mask-RCNN
https://github.com/yangyucheng000/RefineNet
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
https://github.com/yangyucheng000/StackedHourglass
https://github.com/yangyucheng000/University/tree/main/model-3/segformer
Segformer++: Efficient Token-Merging Strategies for High-Resolution Semantic Segmentation
https://github.com/yangyucheng000/ViPNAS
ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search
https://github.com/yangyucheng000/maskrcnn_mobilenetv1
https://github.com/yangyucheng000/unipose-mindspore
UniPose: Unified Human Pose Estimation in Single Images and Videos
https://github.com/yasutomo57jp/openpifpaf_ros
OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association
https://github.com/yczhang1017/SSD_resnet_pytorch
https://github.com/yehengchen/FaceNet-FaceRecognition
https://github.com/yehengchen/FaceRecognition
https://github.com/yehengchen/FaceRecognition-FaceNet
https://github.com/yehengchen/SmartCar-FaceRecognition
https://github.com/yenchenlin/inerf-public
https://github.com/yfeng95/deca
Learning an Animatable Detailed 3D Face Model from In-The-Wild Images
https://github.com/yhjo09/VSR-DUF
https://github.com/yhw-yhw/show
https://github.com/yifan123/IC-Conv
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.