https://github.com/Xylon-Sean/rfnet
RF-Net: An End-to-End Image Matching Network based on Receptive Field
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/YQRickWang/Motion_Prediction
https://github.com/YQRickWang/tf
https://github.com/YangChangHee/ICCV2023_SEFD_RELEASE
https://github.com/YangYangTaoTao/V2V-PoseNet_Pytorch
https://github.com/YanyingZH/srgan-esrgan-pytorch
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/YapengTian/TDAN-VSR-CVPR-2020
TDAN: Temporally Deformable Alignment Network for Video Super-Resolution
https://github.com/YapengTian/TDAN_VSR
TDAN: Temporally Deformable Alignment Network for Video Super-Resolution
https://github.com/YeongHyeon/Super-Resolution_CNN
https://github.com/YeongHyeon/Super-Resolution_CNN-PyTorch
https://github.com/Yongjjun/singleshotpose
https://github.com/Yotonctu/densefusion_torch1.0
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
https://github.com/YoungXIAO13/PoseFromShape
Learning Local RGB-to-CAD Correspondences for Object Pose Estimation
https://github.com/YuhangMing/Object-Guided-Relocalisation
Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation
https://github.com/Yuki0114/UndergraduateThesis_SRCNN_keras
https://github.com/YvanG/VQGAN-CLIP
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/ZHONGCHUYUN/3d_pose_baseline_tf
A simple yet effective baseline for 3d human pose estimation
https://github.com/ZJX9999/art_track
https://github.com/ZQPei/deep_sort_pytorch
https://github.com/ZainRaza14/Neural-ODE
https://github.com/Zero-nnkn/ml-utils/blob/main/cv/pose/openpose_predictor.py
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/ZhangNYG/ST-GCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/Zhangyanbo/iResNetLab
https://github.com/ZhaoHengrun/EDVR
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
https://github.com/ZhixinLai/3D-detection-with-monocular-RGB-image
https://github.com/ZiqiaoPeng/EmoTalk
EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation
https://github.com/a2824256/singleshotpose_imp
https://github.com/aba450/Super-Resolution
https://github.com/abaietto/neural_ode_classification
https://github.com/abdulwaheedsoudagar/SR-CNN
https://github.com/abhi1kumar/M3D-RPN
M3D-RPN: Monocular 3D Region Proposal Network for Object Detection
https://github.com/abhi1kumar/hrnet_pose_single_gpu
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/adbobes/VideoSuperResolution
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/adipandas/torch_shnet
https://github.com/adnanalam53/cycleGAN
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/agnJason/RGB-D-PIFuHD
PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.