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/ibaiGorordo/ONNX-CenterSnap-6D-Pose-and-Shape-Estimation
https://github.com/ibaiGorordo/Pytorch-KP2D-Feature-Tracking
Neural Outlier Rejection for Self-Supervised Keypoint Learning
https://github.com/ibaiGorordo/Sapiens-Pytorch-Inference
https://github.com/ibm/max-human-pose-estimator
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/iclavera/learning_to_adapt
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning
https://github.com/idealo/image-super-resolution
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/iiyama-lab/PS-VAEs
Partially-Shared Variational Auto-encoders for Unsupervised Domain Adaptation with Target Shift
https://github.com/ilovepose/DarkPose
Distribution-Aware Coordinate Representation for Human Pose Estimation
https://github.com/ilovepose/fast-human-pose-estimation.pytorch
https://github.com/imatge-upc/3D-GAN-superresolution
https://github.com/infantmotion/infantmotion.github.io
Unsupervised Human Pose Estimation through Transforming Shape Templates
https://github.com/infini8-13/MaskRCNN_tryout
https://github.com/inzva/hand-joint-detection
Hand Keypoint Detection in Single Images using Multiview Bootstrapping
https://github.com/ipeter50/graph_ode
https://github.com/iqbalu/PoseTrack-CVPR2017
https://github.com/isaaccorley/torchrs
https://github.com/isarandi/posepile
https://github.com/itsasimiqbal/SeBRe
https://github.com/itsuki8914/ESRGAN-TensorFlow
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/j96w/DenseFusion
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
https://github.com/jacksonlli/ProjectQuarantine_HumanSegmentation
https://github.com/jacobjinkelly/easy-neural-ode
https://github.com/jaehyunnn/ViTPose_pytorch
ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation
https://github.com/jaingaurav3/GAN-Hacks
https://github.com/jaivanti/Super-Resolution-using-ConvNet
https://github.com/jaminfong/fna
FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search
https://github.com/janbertelngo/count-people
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
https://github.com/janblumenkamp/esa-kelvin-pose-estimation
https://github.com/jaroslaw1007/Pose_Baseline_3D_PyTorch
A simple yet effective baseline for 3d human pose estimation
https://github.com/jarrodanderson/densepose
https://github.com/jarrodanderson/openpose-demo
https://github.com/jasjeetIM/Mask-RCNN
https://github.com/jasonyzhang/raydiffusion
https://github.com/jcao216/DAIN_Modified
https://github.com/jeffffffli/CrowdPose
CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.