Papers with Code 아카이브
3D Human Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 9
- 평가표
- 25
- 보존된 평가 기록
- 447
3D Human Pose Estimation is a computer vision task that involves estimating the 3D positions and orientations of body joints and bones from 2D images or videos. The goal is to reconstruct the 3D pose of a person in real-time, which can be used in a variety of applications, such as virtual reality, human-computer interaction, and motion analysis.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mtorabirad/PricePrediction
https://github.com/muelea/tuch
https://github.com/mukul1093/Image-Super-Resolution
https://github.com/murrman95/INF573Project2018
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
https://github.com/mv-lab/swin2sr
https://github.com/nabarunbaruaAIML/ATCC_Yolov5
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/nagadomi/waifu2x
https://github.com/namedBen/Convolutional-Pose-Machines-Pytorch
https://github.com/nannau/DoWnGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/niazwazir/REAL_TIME_VIDEO_IMSR
https://github.com/nickgkan/3d_diffuser_actor
Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation
https://github.com/nihui/realsr-ncnn-vulkan
https://github.com/nish-97v/3D-human-pose-estimation
Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
https://github.com/nkolot/GraphCMR
Convolutional Mesh Regression for Single-Image Human Shape Reconstruction
https://github.com/nkolot/SPIN
Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop
https://github.com/nntanaka/Fourier-Analysis-for-Skeleton-based-Action-Recognition
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/npielawski/pytorch_tiramisu
https://github.com/nsarafianos/Garment3DGen
https://github.com/nsubbaian/FrequentistML
https://github.com/olaviinha/NeuralImageSuperResolution
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/oli4jansen/VIBE_old
VIBE: Video Inference for Human Body Pose and Shape Estimation
https://github.com/omglu93/energy_consumption_eda_prediction
https://github.com/omkarbhope/Pose-Estimation
Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB
https://github.com/omkarghugarkar007/Neural_Super_Sampling
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/open-mmlab/mmdetection
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/open-mmlab/mmediting/blob/master/configs/restorers/basicvsr/README.md
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
https://github.com/open-mmlab/mmskeleton
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/open-mmlab/mmsr
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
https://github.com/openxrlab/xrmocap
Fast and Robust Multi-Person 3D Pose Estimation from Multiple Views
https://github.com/osmr/imgclsmob
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/osofr/xgboost
https://github.com/ostadabbas/in-bed-pose-estimation
https://github.com/otaheri/GOAL
https://github.com/oublalkhalid/AI-OpenPose
https://github.com/perfanalytics/pose2sim
Fast and Robust Multi-Person 3D Pose Estimation from Multiple Views
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.