Papers with Code 아카이브
Pose Estimation
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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/Ysz2022/Fourier123
공식 코드Hybrid Fourier Score Distillation for Efficient One Image to 3D Object Generation
https://github.com/Yuexiaoxi10/Key-Frame-Proposal-Network-for-Efficient-Pose-Estimation-in-Videos
공식 코드Key Frame Proposal Network for Efficient Pose Estimation in Videos
https://github.com/YuliangXiu/PoseFlow
공식 코드https://github.com/YxZhxn/Ray3D
공식 코드Ray3D: ray-based 3D human pose estimation for monocular absolute 3D localization
https://github.com/ZJUYH/LocNet_caffe
공식 코드LocNet: Global localization in 3D point clouds for mobile vehicles
https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation
공식 코드Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/ZhengZerong/DeepHuman
공식 코드https://github.com/ZhengyiLuo/MEVA
공식 코드3D Human Motion Estimation via Motion Compression and Refinement
https://github.com/Zhongdao/UniTrack
공식 코드Do Different Tracking Tasks Require Different Appearance Models?
https://github.com/ZhouZijie77/SegLocNet
공식 코드SegLocNet: Multimodal Localization Network for Autonomous Driving via Bird's-Eye-View Segmentation
https://github.com/ZikangYuan/sr_livo
공식 코드SR-LIVO: LiDAR-Inertial-Visual Odometry and Mapping with Sweep Reconstruction
https://github.com/Zrrr1997/Pose2Drone
공식 코드Pose2Drone: A Skeleton-Pose-based Framework for Human-Drone Interaction
https://github.com/a-nau/tampar
공식 코드TAMPAR: Visual Tampering Detection for Parcel Logistics in Postal Supply Chains
https://github.com/aaditagarwal/ActiveNet
공식 코드ActiveNet: A computer-vision based approach to determine lethargy
https://github.com/aaronwool/keygnet
공식 코드Learning Better Keypoints for Multi-Object 6DoF Pose Estimation
https://github.com/aaronwool/rcvpose
공식 코드Vote from the Center: 6 DoF Pose Estimation in RGB-D Images by Radial Keypoint Voting
https://github.com/aaronwool/rcvpose3d
공식 코드Keypoint Cascade Voting for Point Cloud Based 6DoF Pose Estimation
https://github.com/aau-cns/poet
공식 코드PoET: Pose Estimation Transformer for Single-View, Multi-Object 6D Pose Estimation
https://github.com/aberdeenml/equicaps
공식 코드EquiCaps: Predictor-Free Pose-Aware Pre-Trained Capsule Networks
https://github.com/abhi1kumar/SUP-NeRF
공식 코드https://github.com/activevisionlab/dfnet
공식 코드DFNet: Enhance Absolute Pose Regression with Direct Feature Matching
https://github.com/acuooooo/resmatch
공식 코드ResMatch: Residual Attention Learning for Local Feature Matching
https://github.com/adaptivemotorcontrollab/modelzoo-figures
공식 코드SuperAnimal pretrained pose estimation models for behavioral analysis
https://github.com/adjkamel/goa_bat
공식 코드3D Human Pose Estimation via Spatial Graph Order Attention and Temporal Body Aware Transformer
https://github.com/adwardlee/renderih
공식 코드RenderIH: A Large-scale Synthetic Dataset for 3D Interacting Hand Pose Estimation
https://github.com/aerialroboticsgroup/forest-vo
공식 코드ForestVO: Enhancing Visual Odometry in Forest Environments through ForestGlue
https://github.com/aghasemzadeh/openmpl
공식 코드https://github.com/ahmad-573/vurf
공식 코드VURF: A General-purpose Reasoning and Self-refinement Framework for Video Understanding
https://github.com/ahmedheakl/fish-counting
공식 코드Precision Aquaculture: An Integrated Computer Vision and IoT Approach for Optimized Tilapia Feeding
https://github.com/ahundt/costar_dataset
공식 코드The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints
https://github.com/ahundt/enas
공식 코드The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints
https://github.com/ai-application-and-integration-lab/rdpn6d
공식 코드https://github.com/ailingzengzzz/Split-and-Recombine-Net
공식 코드SRNet: Improving Generalization in 3D Human Pose Estimation with a Split-and-Recombine Approach
https://github.com/aim-uofa/genpercept
공식 코드What Matters When Repurposing Diffusion Models for General Dense Perception Tasks?
https://github.com/aim-uofa/poseur
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.