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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/huochf/StackFLOW

Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild

https://github.com/hz-ants/DensePose

DensePose: Dense Human Pose Estimation In The Wild

https://github.com/hz-ants/betapose

Estimating 6D Pose From Localizing Designated Surface Keypoints

https://github.com/hz-ants/obtain-an-object-mesh-and-create-labels

Real-Time Seamless Single Shot 6D Object Pose Prediction

https://github.com/hz-ants/segmentation-driven-pose-train-

Estimating 6D Pose From Localizing Designated Surface Keypoints

https://github.com/hz-ants/yolo-6d

Real-Time Seamless Single Shot 6D Object Pose Prediction

https://github.com/idealo/image-super-resolution

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/imatge-upc/3D-GAN-superresolution

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

https://github.com/isaaccorley/torchrs

Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks

https://github.com/isarandi/posepile

Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats

https://github.com/itsuki8914/ESRGAN-TensorFlow

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/jaingaurav3/GAN-Hacks

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

https://github.com/jaivanti/Super-Resolution-using-ConvNet

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/janblumenkamp/esa-kelvin-pose-estimation

Convolutional Pose Machines

https://github.com/jaroslaw1007/Pose_Baseline_3D_PyTorch

A simple yet effective baseline for 3d human pose estimation

https://github.com/jarrodanderson/densepose

DensePose: Dense Human Pose Estimation In The Wild

https://github.com/jarrodanderson/openpose-demo

Convolutional Pose Machines

https://github.com/jcao216/DAIN_Modified

Video Enhancement with Task-Oriented Flow

https://github.com/jeffffffli/HybrIK

HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation

https://github.com/jiajunhua/facebookresearch-DensePose

DensePose: Dense Human Pose Estimation In The Wild

https://github.com/jiajunhua/facebookresearch-Detectron

DensePose: Dense Human Pose Estimation In The Wild

https://github.com/jiang-du/BlazePose-tensorflow

BlazePose: On-device Real-time Body Pose tracking

https://github.com/jiangzhongkai/ifly-algorithm_challenge

XGBoost: A Scalable Tree Boosting System

https://github.com/jiean001/models_m/tree/main/DecoMR

3D Human Mesh Regression with Dense Correspondence

https://github.com/jimmycv07/DiffIR2VR-Zero

Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution

https://github.com/jixiaozhong/RealSR

https://github.com/jlanday/Language-Detection

XGBoost: A Scalable Tree Boosting System

https://github.com/jlanday/Model-Selection-for-Pion-Photoproduction

XGBoost: A Scalable Tree Boosting System

https://github.com/jlanday/Towards-the-Minimal-Spectrum-of-Excited-Baryons

XGBoost: A Scalable Tree Boosting System

https://github.com/jlanday/X-ray-Object-Classification

XGBoost: A Scalable Tree Boosting System

https://github.com/joezengcbs/mini_xgboost_from_scratch

XGBoost: A Scalable Tree Boosting System

https://github.com/jreisam/Unity-OpenPose-Edutable

Convolutional Pose Machines

https://github.com/jshermeyer/VDSR4Geo

Accurate Image Super-Resolution Using Very Deep Convolutional Networks

https://github.com/jupiterman/Super-Resolution-Images

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/k-miran/hear

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/kadirnar/bsrgan-pip

Designing a Practical Degradation Model for Deep Blind Image Super-Resolution

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