https://github.com/chongchen20/Deeplabcut
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
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/chonyy/AI-basketball-analysis
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/chrischoy/FCGF
https://github.com/chronoshell/copyydscsv
HybridPose: 6D Object Pose Estimation under Hybrid Representations
https://github.com/chuanqichen/deepcoaching
https://github.com/chuxiaoselena/SparsenessMeetsDeepness
Sparseness Meets Deepness: 3D Human Pose Estimation from Monocular Video
https://github.com/cientgu/instructdiffusion
InstructDiffusion: A Generalist Modeling Interface for Vision Tasks
https://github.com/classner/up
Unite the People: Closing the Loop Between 3D and 2D Human Representations
https://github.com/cmbagent/cmbagent
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
https://github.com/cmbagents/cmbagent
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
https://github.com/cmitash/physim-dataset-generator
https://github.com/cmsflash/efficient-attention
https://github.com/cmu-enyac/Renofeation
https://github.com/code-implementation1/Code2/tree/main/AlphaPose
AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time
https://github.com/code-implementation1/Code3/tree/main/ESRGAN
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
https://github.com/code-implementation1/Code4/tree/main/HRNetV2
https://github.com/code-implementation1/Code4/tree/main/HRNetW48_cls
https://github.com/code-implementation1/Code6/tree/main/Non_local
https://github.com/code-implementation1/Code6/tree/main/PAMTRI
https://github.com/code-implementation1/Code6/tree/main/ras
Recurrent Back-Projection Network for Video Super-Resolution
https://github.com/code-implementation1/Code7/tree/main/RefineNet
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
https://github.com/code-implementation1/Code8/tree/main/simple_baselines
https://github.com/code-implementation1/Code8/tree/main/simple_pose
Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation
https://github.com/code-implementation1/Code9/tree/main/EDSR
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
https://github.com/code-implementation1/Code9/tree/main/Efficientnet
EfficientHRNet: Efficient Scaling for Lightweight High-Resolution Multi-Person Pose Estimation
https://github.com/code-implementation1/Code9/tree/main/MaskRCNN/maskrcnn_mobilenetv1
https://github.com/code-implementation1/Code9/tree/main/ShuffleNet
https://github.com/code-implementation1/Code9/tree/main/hrnet
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
https://github.com/cokowpublic/Nuclei-Segmentation
https://github.com/collectionslab/Omniscribe
https://github.com/collectionslab/annotations-computervision
https://github.com/collectionslab/book-annotation-classification
https://github.com/conceptofmind/Region-ViT-flax
RegionViT: Regional-to-Local Attention for Vision Transformers
https://github.com/copperwire/neural_ode
https://github.com/coulsonlee/dy-dca-eccv2024
Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting
https://github.com/crowdai/crowdai-mapping-challenge-mask-rcnn
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.