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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/shreeyashyende/better_img_res_with_SRCNN

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/shreyashampali/HOnnotate

HOnnotate: A method for 3D Annotation of Hand and Object Poses

https://github.com/shreyashampali/ho3d

HOnnotate: A method for 3D Annotation of Hand and Object Poses

https://github.com/shumming/GLE_FLD

Fashion Landmark Detection in the Wild

https://github.com/shunsukesaito/PIFu

PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization

https://github.com/shuuchen/HRNet

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/shuuchen/PoseWarper.pytorch

Learning Temporal Pose Estimation from Sparsely-Labeled Videos

https://github.com/sidify/3d-pose-estimation

A simple yet effective baseline for 3d human pose estimation

https://github.com/sidsunny/human-pose-estimation-mlsp

A simple yet effective baseline for 3d human pose estimation

https://github.com/simochen/flowtrack.pytorch

Simple Baselines for Human Pose Estimation and Tracking

https://github.com/sithu31296/pose-estimation

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/sithu31296/semantic-segmentation

Polarized Self-Attention: Towards High-quality Pixel-wise Regression

https://github.com/siyliepfl/deformation-aware-unpaired-image-translation

Stacked Hourglass Networks for Human Pose Estimation

https://github.com/siyuhuang/TOD

Learning Loss for Active Learning

https://github.com/sizhean/panohead

WHENet: Real-time Fine-Grained Estimation for Wide Range Head Pose

https://github.com/sjtuytc/segmentation-driven-pose

Estimating 6D Pose From Localizing Designated Surface Keypoints

https://github.com/skchen1993/SwinIR

SwinIR: Image Restoration Using Swin Transformer

https://github.com/skhadem/3D-BoundingBox

3D Bounding Box Estimation Using Deep Learning and Geometry

https://github.com/slryou41/AnchorLoss

Anchor Loss: Modulating Loss Scale based on Prediction Difficulty

https://github.com/smallcorgi/3D-Deepbox

3D Bounding Box Estimation Using Deep Learning and Geometry

https://github.com/smartadpole/spider

AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time

https://github.com/smellslikeml/YogAI

Convolutional Pose Machines

https://github.com/snt-arg/lidar_situational_graphs

Situational Graphs for Robot Navigation in Structured Indoor Environments

https://github.com/soeaver/RP-R-CNN

Parsing R-CNN for Instance-Level Human Analysis

https://github.com/soham239/MLND_Capstone_Project

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/sooyekim/3DSRnet

3DSRnet: Video Super-resolution using 3D Convolutional Neural Networks

https://github.com/soran-ghaderi/torchebm

Energy-Based Models for Deep Probabilistic Regression

https://github.com/soulguy/Faster-rcnn

https://github.com/soulslicer/staf

Convolutional Pose Machines

https://github.com/sourabhagrawal23/human-action-classification

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

https://github.com/spurra/vae-hands-3d

Cross-modal Deep Variational Hand Pose Estimation

https://github.com/srini2dl/DogPoseEstimation

DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model

https://github.com/srpkdyy/VideoLDM

Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models

https://github.com/ssanyachetwani/Fashion-Trend-Detection-and-Recommendation-Model

https://github.com/ssulun/pytorch-pixelshuffle1d

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

https://github.com/stanleybak/nnenum

Verification of Deep Convolutional Neural Networks Using ImageStars

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