Papers with Code 아카이브
3D Human Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
- 25
- 보존된 평가 기록
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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/visionNoob/hrnet_pytorch
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/vladmandic/blazepose
https://github.com/vnmr/JointVideoPose3D
3D human pose estimation in video with temporal convolutions and semi-supervised training
https://github.com/vpaliwal1/Deep_learning_SRCNN
https://github.com/vscoboy/AI-
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/vuanhtu1993/Keras-SRGANs
https://github.com/wangzt-halo/das
Distribution-Aware Single-Stage Models for Multi-Person 3D Pose Estimation
https://github.com/wh1h/DNLN
https://github.com/why2011btv/6d_pose_estimation
Estimating 6D Pose From Localizing Designated Surface Keypoints
https://github.com/why986/VFA
MMFace4D: A Large-Scale Multi-Modal 4D Face Dataset for Audio-Driven 3D Face Animation
https://github.com/wqyin/smplest-x
SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation
https://github.com/wsjzha/deep-high-resolution-net.pytorch
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/wu-cvgl/mvcontrol
Controllable Text-to-3D Generation via Surface-Aligned Gaussian Splatting
https://github.com/wustl-cig/FLAIR
FLAIR: A Conditional Diffusion Framework with Applications to Face Video Restoration
https://github.com/wwhio/megmodels
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/xaoch/rapJetson2
https://github.com/xar47x/pose
https://github.com/xg416/DATUM
Recurrent Video Restoration Transformer with Guided Deformable Attention
https://github.com/xgd/waifu2xx
https://github.com/xiadanqing/Binary
https://github.com/xiaozeguo123/-Quick-Capture-system-HSR
https://github.com/xingyizhou/pytorch-pose-hg-3d
Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
https://github.com/xinntao/BasicSR
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
https://github.com/xiuyu0000/new_papers_codes/tree/main/CLIFF
CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation
https://github.com/xiuyu0000/new_papers_codes/tree/main/DecoMR
https://github.com/xiuyu0000/new_papers_codes/tree/main/cmr
Convolutional Mesh Regression for Single-Image Human Shape Reconstruction
https://github.com/xiuyu0000/new_papers_codes/tree/main/rbpn
Recurrent Back-Projection Network for Video Super-Resolution
https://github.com/xn1997/HigherHRnet
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
https://github.com/xoraus/Super-Resolution-CNN-for-Image-Restoration
https://github.com/xuxy09/texformer
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning
https://github.com/xxlong0/wonder3d
https://github.com/xzr12/PredCNN
https://github.com/yakhyo/head-pose-estimation
6D Rotation Representation For Unconstrained Head Pose Estimation
https://github.com/yangyucheng000/ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/yangyucheng000/RefineNet
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.