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/kirumang/Pix2Pose
Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation
https://github.com/kishansharma3012/Deep_Head_Pose_Estimation
https://github.com/kohillyang/mx-detection
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/koi953215/NaRCan
Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution
https://github.com/kolkir/coarse_loftr_trt
LoFTR: Detector-Free Local Feature Matching with Transformers
https://github.com/kornia/kornia
https://github.com/krantirk/py-faster-rcnn
https://github.com/krishnac7/OpenPose
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/krutikabapat/Deep-Learning-based-OpenPose-Estimation
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/ktoyod/rotatedpose
Post-Data Augmentation to Improve Deep Pose Estimation of Extreme and Wild Motions
https://github.com/kukby/Mish-semantic-segmentation-pytorch
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/kunjan-mhaske/Image-super-resolution-model-using-4-layers-CNN-from-PyTorch
https://github.com/kwantommy/breast-cancer-diagnosis
https://github.com/l13025816/PGCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/l3th4l/SuperRes
https://github.com/landskape-ai/triplet-attention
https://github.com/laobaiswag/openpose1
https://github.com/laomao0/BIN
https://github.com/laowang666888/ECSP1
CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark
https://github.com/laowang666888/HRNET
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/laowng/GISR
https://github.com/lastbasket/endo-4dgs
Learning How To Robustly Estimate Camera Pose in Endoscopic Videos
https://github.com/laura-a-n-n/tf-garf
GARF: Gaussian Activated Radiance Fields for High Fidelity Reconstruction and Pose Estimation
https://github.com/lcxzzz0113/CenterNet
https://github.com/lee-aaron/Image-Super-Resolution
https://github.com/lee-man/movenet
https://github.com/leeyegy/simcc
https://github.com/leftthomas/espcn
https://github.com/leob03/E2E_Object_Pose_Estimator
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
https://github.com/leobean/CenterNet_oriented
https://github.com/leochangzliao/OPBM
https://github.com/leonardhan1979/fasterRCNN
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/fastvit
FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/moganet
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/uniformer
UniFormer: Unifying Convolution and Self-attention for Visual Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.