Papers with Code 아카이브
Monocular 3D Human Pose Estimation
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This task targets at 3D human pose estimation with a single RGB camera.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ostadabbas/adaptedhumanpose
공식 코드Adapted Human Pose: Monocular 3D Human Pose Estimation with Zero Real 3D Pose Data
https://github.com/ostadabbas/hw-hup
공식 코드https://github.com/paTRICK-swk/Pose3D-RIE
공식 코드Improving Robustness and Accuracy via Relative Information Encoding in 3D Human Pose Estimation
https://github.com/patrick-swk/d3dp
공식 코드Diffusion-Based 3D Human Pose Estimation with Multi-Hypothesis Aggregation
https://github.com/patrick-swk/p-stmo
공식 코드P-STMO: Pre-Trained Spatial Temporal Many-to-One Model for 3D Human Pose Estimation
https://github.com/paulchhuang/bstro
공식 코드https://github.com/paulchhuang/rich_toolkit
공식 코드https://github.com/pixelite1201/agora_evaluation
공식 코드AGORA: Avatars in Geography Optimized for Regression Analysis
https://github.com/seblutz/JointFormer
공식 코드https://github.com/senakicir/ActiveMoCap
공식 코드ActiveMoCap: Optimized Viewpoint Selection for Active Human Motion Capture
https://github.com/ssfootball04/generative_pose
공식 코드Monocular 3D Human Pose Estimation by Generation and Ordinal Ranking
https://github.com/sunnychencool/Anatomy3D
공식 코드Anatomy-aware 3D Human Pose Estimation with Bone-based Pose Decomposition
https://github.com/taatiteam/motionagformer
공식 코드MotionAGFormer: Enhancing 3D Human Pose Estimation with a Transformer-GCNFormer Network
https://github.com/tobibaum/partialsportsfieldreg_3dhpe
공식 코드Monocular 3D Human Pose Estimation for Sports Broadcasts using Partial Sports Field Registration
https://github.com/twehrbein/Probabilistic-Monocular-3D-Human-Pose-Estimation-with-Normalizing-Flows
공식 코드Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows
https://github.com/una-dinosauria/3d-pose-baseline
공식 코드A simple yet effective baseline for 3d human pose estimation
https://github.com/vegesm/siamese-pose-estimation
공식 코드https://github.com/xingyizhou/pose-hg-3d
공식 코드Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
https://github.com/yuliangxiu/icon
공식 코드https://github.com/zczcwh/PoseFormer
공식 코드3D Human Pose Estimation with Spatial and Temporal Transformers
https://github.com/2023-MindSpore-1/ms-code-27
https://github.com/2023-MindSpore-4/Code-5/tree/main/HMR
https://github.com/ARMUGHAN-SHAHID/MoboDensepose
https://github.com/AkashGanesan/PedestrianAttention
https://github.com/CMU-Perceptual-Computing-Lab/MonocularTotalCapture
Monocular Total Capture: Posing Face, Body, and Hands in the Wild
https://github.com/Daniil-Osokin/lightweight-human-pose-estimation-3d-demo.pytorch
XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera
https://github.com/DenisTome/Lifting-from-the-Deep-release
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
https://github.com/ECE740F21T01/pytorch-pose-hg-3d
Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
https://github.com/Liuxiang0358/HMR
https://github.com/M-Usman10/DenseSqueeze-RCNN
https://github.com/MandyMo/pytorch_HMR
Unite the People: Closing the Loop Between 3D and 2D Human Representations
https://github.com/ManifoldFR/recvis-project
https://github.com/MindSpore-paper-code-3/code4/tree/main/HMR
https://github.com/SJTU-DL-lab/3d-pose-baseline
A simple yet effective baseline for 3d human pose estimation
https://github.com/StupidmanTan/facebookresearch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.