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/stillarrow/S2VT_ACT
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/stimong/DensePose_python3
https://github.com/strawberryfg/c2f-3dhm-human-caffe
Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose
https://github.com/strivebo/image_segmentation_dl
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/sudarsanGyrus/3D_Pose_VIBE
VIBE: Video Inference for Human Body Pose and Shape Estimation
https://github.com/svikramank/DensePose
https://github.com/swathi-469/Teresa-Bot
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
https://github.com/tamasino52/Any-GCN
Semantic Graph Convolutional Networks for 3D Human Pose Regression
https://github.com/tamasino52/GraphSH
Graph Stacked Hourglass Networks for 3D Human Pose Estimation
https://github.com/tamasino52/U-CondDGCN
Conditional Directed Graph Convolution for 3D Human Pose Estimation
https://github.com/tamasino52/UGCN
https://github.com/teakkkz/imageSR
https://github.com/techforgood-kiran/ai
https://github.com/tegg89/SRCNN-Tensorflow
https://github.com/tetrachrome/subpixel
https://github.com/theyellowdiary/openpose
https://github.com/thomasslloyd/FitSpatial
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/thoughtmachines/Human-Pose-Estimation-using-HRNets
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/thstkdgus35/EDSR-PyTorch
https://github.com/thuml/ContextWM
Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose
https://github.com/thuxyz19/test
3D Human Pose Estimation with Spatial and Temporal Transformers
https://github.com/tianyuan168326/videosemanticcompression-pytorch
XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
https://github.com/tinnunculus/Denoising_SR
https://github.com/titu1994/Image-Super-Resolution
https://github.com/tom-doerr/TecoGAN
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/tom-doerr/TecoGAN-Docker
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/tqchen/xgboost
https://github.com/transpchan/Live3D-v2
https://github.com/twhui/SRGAN-PyTorch
https://github.com/tyshiwo/DRRN_CVPR17
https://github.com/u7javed/Image-Enhancer-via-ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/u7javed/Image-Super-Resolution-Enhancer-via-ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/ubc-vision/DwNet
https://github.com/v1viswan/Domain_adaptation_in_HRNet
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/vanoracai/Exploiting-Spatial-temporal-Relationships-for-3D-Pose-Estimation-via-Graph-Convolutional-Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.