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Pose Estimation

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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/Championchess/Generative-3D-Part-Assembly

Generative 3D Part Assembly via Dynamic Graph Learning

https://github.com/ChenyanWu/MEBOW

MEBOW: Monocular Estimation of Body Orientation In the Wild

https://github.com/Chenzhoujia/190916-hand3d-CNN-GNN

Learning to Estimate 3D Hand Pose from Single RGB Images

https://github.com/Chenzixi1/D4LCNpaddle

Learning Depth-Guided Convolutions for Monocular 3D Object Detection

https://github.com/ChetanTayal138/HAR-Web

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

https://github.com/Chris10M/Ev2Hands

3D Pose Estimation of Two Interacting Hands from a Monocular Event Camera

https://github.com/Cjmmmmmm/BreastUltrasound_Detection

Objects as Points

https://github.com/ClePol/CPM_Presentation

Convolutional Pose Machines

https://github.com/ClementPinard/SfmLearner-Pytorch

Unsupervised Learning of Depth and Ego-Motion from Video

https://github.com/Cli98/pytorch-cpn

Cascaded Pyramid Network for Multi-Person Pose Estimation

https://github.com/ColdNoodler/py-faster-rcnn-cuda10

https://github.com/Coldog2333/pytoflow

Video Enhancement with Task-Oriented Flow

https://github.com/CompVis/unsupervised-part-segmentation

Unsupervised Part-Based Disentangling of Object Shape and Appearance

https://github.com/ConnorChristie/image-to-3d-bbox

3D Bounding Box Estimation Using Deep Learning and Geometry

https://github.com/CorentinMAG/SRGAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/CuberrChen/HumanPoseBL-Paddle

Simple Baselines for Human Pose Estimation and Tracking

https://github.com/CyberPegasus/KUKA_Catch_Project

SegICP: Integrated Deep Semantic Segmentation and Pose Estimation

https://github.com/D1vyansh/BodyJointDetection

Convolutional Pose Machines

https://github.com/DC1991/FS_Net

FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism

https://github.com/DELTA37/LatentKeypointGAN

LatentKeypointGAN: Controlling Images via Latent Keypoints

https://github.com/DagnyT/GLAMpoints-PyTorch

GLAMpoints: Greedily Learned Accurate Match points

https://github.com/Daniil-Osokin/gccpm-look-into-person-cvpr19.pytorch

Convolutional Pose Machines

https://github.com/Daniil-Osokin/lightweight-human-pose-estimation-3d-demo.pytorch

Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB

https://github.com/Daniil-Osokin/lightweight-human-pose-estimation.pytorch

Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose

https://github.com/DaphneAntotsiou/task-oriented-hand-retargeting

Spatial Attention Deep Net with Partial PSO for Hierarchical Hybrid Hand Pose Estimation

https://github.com/Darius-Liesis/HRNet-works

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

https://github.com/DarshanDeshpande/jax-models

Visual Attention Network

https://github.com/DavHoffmann/Multi-humanDataGeneration

Learning to Train with Synthetic Humans

https://github.com/DeNA/Chainer_Mask_R-CNN

https://github.com/DengpanFu/LUPerson

Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks

https://github.com/DenisTome/Lifting-from-the-Deep-release

Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image

https://github.com/Dictator890/ESRGAN-Android-Demo

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/DivJAth/DeepLearning5922

https://github.com/DixinFan/st-gcn

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/Djmcflush/Funky-Movement

HP-GAN: Probabilistic 3D human motion prediction via GAN

https://github.com/DrChainsaw/neuralODE4j

Neural Ordinary Differential Equations

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