https://github.com/lizhuoq/ESRGAN-pytorch
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Papers with Code 아카이브
3D Human Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 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/llSourcell/3D_Pose_Estimation
A simple yet effective baseline for 3d human pose estimation
https://github.com/lncarter/Dencepose
https://github.com/lncarter/Openpose
https://github.com/lrxjason/Attention3DHumanPose
https://github.com/lucasmfaria/Machine-Learning-Capstone
https://github.com/luciennnnnnn/dualformer
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/lugiavn/transformation-transferring
Let's Transfer Transformations of Shared Semantic Representations
https://github.com/lwxGitHub123/openpose
https://github.com/lwxGitHub123/openpose20200610
https://github.com/markmaxt/VideoSR
https://github.com/mattiagaggi/3D-Pose-Estimator---MSc_project
Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation
https://github.com/mayank-17/Project-Image-Restoration-using-SRCNN
https://github.com/mayorquinmachines/YogAI
https://github.com/med-seg/kidney
https://github.com/med-seg/kidney-mc
https://github.com/mediatek-neuropilot/mai22-real-time-video-sr
https://github.com/megvii-research/conr
https://github.com/megvii-research/ijcai2023-conr
https://github.com/mehtadushy/SelecSLS-Pytorch
XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera
https://github.com/mengyingfei/pose-3d-pytorch-ros
Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
https://github.com/metrics-lab/st-fmri
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/mgolnezhad/openpose
https://github.com/michael13162/DoodleGAN
https://github.com/midas-research/text2facegan
Text2FaceGAN: Face Generation from Fine Grained Textual Descriptions
https://github.com/mikeshihyaolin/pose-hg-3d-preprocessing
Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
https://github.com/minar09/MGN
https://github.com/minar09/MGN-Py3
https://github.com/minar09/MultiGarmentNetworkPython3
https://github.com/mindspore-ai/contrib/tree/master/intern/pixelshuffle1d
https://github.com/mindspore-ai/models/tree/master/official/cv/srcnn
https://github.com/mindspore-ai/models/tree/master/research/cv/ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/mindspore-ai/models/tree/master/research/cv/RefineNet
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
https://github.com/mindspore-lab/mindone
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/mks0601/PoseFix_RELEASE
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/mohammadreyaz/openposered
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.