Papers with Code 아카이브

3D Human Pose Estimation

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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/HighVoltageRocknRoll/sr

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/HritwikEkade/Image-Resolution

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/HyeongJu916/Boaz-SR-ESRGAN-PyTorch

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/JDAI-CV/DSD-SATN

Human Mesh Recovery from Monocular Images via a Skeleton-disentangled Representation

https://github.com/Javihaus/Auto-ML-app-with-dash

XGBoost: A Scalable Tree Boosting System

https://github.com/Jia-Research-Lab/Simple-SR

MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution

https://github.com/JonathanLehner/TecoGan-downloader

Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation

https://github.com/Jtoo/fitting_human_smpl_model

Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image

https://github.com/KPIxLILU/Machine-Learning-Workshop

XGBoost: A Scalable Tree Boosting System

https://github.com/Kaif10/Pose-Detection

Convolutional Pose Machines

https://github.com/KarinaKorsgaard/Bartender-AI-OpenPose-Osc-Sender

Convolutional Pose Machines

https://github.com/KeremTurgutlu/papers

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/Khenu/Computer-Animation

3D Human Pose Machines with Self-supervised Learning

https://github.com/KlabCMU/MEVA

3D Human Motion Estimation via Motion Compression and Refinement

https://github.com/KrisLee512/ST-GCN

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/LEEPEIQIN/EDSR

Deep Back-Projection Networks For Super-Resolution

https://github.com/LONG-9621/body_net

BodyNet: Volumetric Inference of 3D Human Body Shapes

https://github.com/LWChen20/RCAN

Deep Back-Projection Networks For Super-Resolution

https://github.com/LiuShenLan/HRNet

HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation

https://github.com/Liuxiang0358/HMR

End-to-end Recovery of Human Shape and Pose

https://github.com/Lornatang/DRRN-PyTorch

Accurate Image Super-Resolution Using Very Deep Convolutional Networks

https://github.com/Lornatang/ESPCN-PyTorch

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/Lornatang/ESRGAN-PyTorch

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/Lornatang/FSRCNN-PyTorch

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/Lornatang/SRCNN-PyTorch

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/Lornatang/VDSR-PyTorch

Accurate Image Super-Resolution Using Very Deep Convolutional Networks

https://github.com/M-Usman10/DenseSqueeze-RCNN

DensePose: Dense Human Pose Estimation In The Wild

https://github.com/MLX15/TecoGAN

Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation

https://github.com/MS-P3/code4/tree/main/PPT

PPT: token-Pruned Pose Transformer for monocular and multi-view human pose estimation

https://github.com/MS-P3/code5/tree/main/graphormer

Mesh Graphormer

https://github.com/MandyMo/pytorch_HMR

Unite the People: Closing the Loop Between 3D and 2D Human Representations

https://github.com/ManifoldFR/recvis-project

End-to-end Recovery of Human Shape and Pose

https://github.com/MarkPrecursor/SRCNN-keras

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/Mary-xl/HRnet_Kaggle_iNat2019_FGVC

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/MatteoT90/WibergianLearning

Rethinking Pose in 3D: Multi-stage Refinement and Recovery for Markerless Motion Capture

https://github.com/MatthewHowe/WIBAM

Weakly Supervised Training of Monocular 3D Object Detectors Using Wide Baseline Multi-view Traffic Camera Data

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.