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/zhouxian/act3d-chained-diffuser
공식 코드Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation
https://github.com/zhujunli1993/swindepose
공식 코드Depth-based 6DoF Object Pose Estimation using Swin Transformer
https://github.com/zhuyue0324/decanus-to-legatus
공식 코드Decanus to Legatus: Synthetic training for 2D-3D human pose lifting
https://github.com/zhyhan/transpar
공식 코드Learning Transferable Parameters for Unsupervised Domain Adaptation
https://github.com/ziangcao0312/PhysX
공식 코드https://github.com/zibin6/lopet
공식 코드Line-based 6-DoF Object Pose Estimation and Tracking With an Event Camera
https://github.com/ziniuwan/maed
공식 코드Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose Estimation
https://github.com/zinsmatt/3D-Aware-Ellipses-for-Visual-Localization
공식 코드3D-Aware Ellipse Prediction for Object-Based Camera Pose Estimation
https://github.com/ziqin-h/givepose
공식 코드https://github.com/zitiantang/thermal-im
공식 코드What Happened 3 Seconds Ago? Inferring the Past with Thermal Imaging
https://github.com/zitronian/6dposediffusion
공식 코드Particle-based 6D Object Pose Estimation from Point Clouds using Diffusion Models
https://github.com/ziyizhang27/dite-hrnet
공식 코드Dite-HRNet: Dynamic Lightweight High-Resolution Network for Human Pose Estimation
https://github.com/zj-binxia/ssl
공식 코드Structured Sparsity Learning for Efficient Video Super-Resolution
https://github.com/zju3dv/DeFlowSLAM
공식 코드D$^3$FlowSLAM: Self-Supervised Dynamic SLAM with Flow Motion Decomposition and DINO Guidance
https://github.com/zju3dv/DetectorFreeSfM
공식 코드https://github.com/zju3dv/LoFTR
공식 코드LoFTR: Detector-Free Local Feature Matching with Transformers
https://github.com/zju3dv/OnePose
공식 코드https://github.com/zju3dv/PVO
공식 코드https://github.com/zju3dv/closemocap
공식 코드https://github.com/zju3dv/disprcnn
공식 코드Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation
https://github.com/zju3dv/gcasp
공식 코드Generative Category-Level Shape and Pose Estimation with Semantic Primitives
https://github.com/zju3dv/iMoCap
공식 코드https://github.com/zjuluolun/bvmatch
공식 코드BVMatch: Lidar-based Place Recognition Using Bird's-eye View Images
https://github.com/zmzhang1998/Real-RawVSR
공식 코드Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark Dataset
https://github.com/zongdai/EditingForDNN
공식 코드Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data Augmentation
https://github.com/zqyq/Unsynchronized-Multi-Camera-Surveillance---IEEE-TNNLS
공식 코드Single-Frame based Deep View Synchronization for Unsynchronized Multi-Camera Surveillance
https://github.com/zray26/dr-pose
공식 코드https://github.com/zshn25/pc4consistentdepth
공식 코드Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-motion
https://github.com/zubair-irshad/Awesome-Implicit-NeRF-Robotics
공식 코드https://github.com/zubair-irshad/shapo
공식 코드ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimization
https://github.com/zxcqlf/robotcar_depthgt_generate
공식 코드Unsupervised Monocular Depth Estimation in Highly Complex Environments
https://github.com/zxhuang97/medor
공식 코드Mesh-based Dynamics with Occlusion Reasoning for Cloth Manipulation
https://github.com/zxz267/HaMuCo
공식 코드HaMuCo: Hand Pose Estimation via Multiview Collaborative Self-Supervised Learning
https://gitlab.com/Percipiote/SimpleDepthPose
공식 코드SimpleDepthPose: Fast and Reliable Human Pose Estimation with RGBD-Images
https://gitlab.com/Percipiote/VoxelKeypointFusion
공식 코드VoxelKeypointFusion: Generalizable Multi-View Multi-Person Pose Estimation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.