Papers with Code 아카이브

Pose Estimation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 17
평가표: 31
보존된 평가 기록: 235

Pose Estimation is a computer vision task where the goal is to detect the position and orientation of a person or an object. Usually, this is done by predicting the location of specific keypoints like hands, head, elbows, etc. in case of Human Pose Estimation.

A common benchmark for this task is MPII Human Pose

<span style="color:grey; opacity: 0.6">( Image credit: Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Krupal09/MaskRCNN-Demo

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/LRacoci/permutation-graphml

Non-local Neural Networks

https://github.com/LWChen20/RCAN

Deep Back-Projection Networks For Super-Resolution

https://github.com/Lakerszjb/CentereNet-C

Objects as Points

https://github.com/Launch-on-Titania/DynOPETs

DynOPETs: A Versatile Benchmark for Dynamic Object Pose Estimation and Tracking in Moving Camera Scenarios

https://github.com/Lauu1023/ODENet

Neural Ordinary Differential Equations

https://github.com/LawrenceZ22/ThermoHands

ThermoHands: A Benchmark for 3D Hand Pose Estimation from Egocentric Thermal Images

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/Lopezurrutia/DSB_2018

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/MIC-DKFZ/DetectionAndRegression

https://github.com/MIC-DKFZ/RegRCNN

https://github.com/MIT-HAN-LAB/temporal-shift-module

Non-local Neural Networks

https://github.com/MIT-SPARK/GNC-and-ADAPT

Outlier-Robust Spatial Perception: Hardness, General-Purpose Algorithms, and Guarantees

https://github.com/MIT-SPARK/STRIDE

Certifiably Optimal Outlier-Robust Geometric Perception: Semidefinite Relaxations and Scalable Global Optimization

https://github.com/MIT-SPARK/TEASER-plusplus

Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection

https://github.com/MLX15/TecoGAN

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

https://github.com/MS-Mind/MS-Code-06/tree/main/Non_local

Non-local Neural Networks

https://github.com/MS-Mind/MS-Code-06/tree/main/PAMTRI

PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data

https://github.com/MS-Mind/MS-Code-06/tree/main/PoseNet

Towards Better Generalization: Joint Depth-Pose Learning without PoseNet

https://github.com/MS-Mind/MS-Code-08/tree/main/Non_local

Non-local Neural Networks

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/MSeeker1340/Vision2018-Pose

Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

https://github.com/MVIG-SJTU/RMPE

Multi-Person Pose Estimation with Local Joint-to-Person Associations

https://github.com/Makunda/DeepLearningASL

https://github.com/Manchery/DeepPose

DeepPose: Human Pose Estimation via Deep Neural Networks

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