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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/boschresearch/symfm6d

공식 코드

SyMFM6D: Symmetry-aware Multi-directional Fusion for Multi-View 6D Object Pose Estimation

https://github.com/boschresearch/what-matters-for-meta-learning

공식 코드

What Matters For Meta-Learning Vision Regression Tasks?

https://github.com/bosmallear/sharp2022

공식 코드

3D Textured Shape Recovery with Learned Geometric Priors

https://github.com/boycehbz/DMMR

공식 코드

Dynamic Multi-Person Mesh Recovery From Uncalibrated Multi-View Cameras

https://github.com/boycehbz/GroupRec

공식 코드

Reconstructing Groups of People with Hypergraph Relational Reasoning

https://github.com/boycehbz/chomp

공식 코드

Occluded Human Body Capture with Self-Supervised Spatial-Temporal Motion Prior

https://github.com/bradleywang0416/gcnext

공식 코드

GCNext: Towards the Unity of Graph Convolutions for Human Motion Prediction

https://github.com/briqr/end2end_humanpose_estimation

공식 코드

Unifying Part Detection and Association for Recurrent Multi-Person Pose Estimation

https://github.com/brown-ivl/ConDor

공식 코드

ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes

https://github.com/bruceyo/GLA-GCN

공식 코드

GLA-GCN: Global-local Adaptive Graph Convolutional Network for 3D Human Pose Estimation from Monocular Video

https://github.com/brunapearson/autoUAV-mtrl

공식 코드

Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments

https://github.com/brunapearson/mtrl-auto-uav

공식 코드

Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments

https://github.com/bryanjvela/poselab3d

공식 코드

PoseBench3D: A Cross-Dataset Analysis Framework for 3D Human Pose Estimation

https://github.com/buptxyb666/AdaptivePose

공식 코드

AdaptivePose++: A Powerful Single-Stage Network for Multi-Person Pose Regression

https://github.com/byeongjun-park/HarmonyView

공식 코드

HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3D

https://github.com/bymaths/probabilistic_triangulation

공식 코드

Probabilistic Triangulation for Uncalibrated Multi-View 3D Human Pose Estimation

https://github.com/bytedance/particle-sfm

공식 코드

ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wild

https://github.com/cai-jianfeng/flowmind2digital

공식 코드

Flowmind2Digital: The First Comprehensive Flowmind Recognition and Conversion Approach

https://github.com/caiyuanhao1998/RSN

공식 코드

Learning Delicate Local Representations for Multi-Person Pose Estimation

https://github.com/caizhongang/SMPLer-X

공식 코드

SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation

https://github.com/calvinyeungck/3d-shot-posture-dataset

공식 코드

AutoSoccerPose: Automated 3D posture Analysis of Soccer Shot Movements

https://github.com/calvinyeungck/athletepose3d

공식 코드

AthletePose3D: A Benchmark Dataset for 3D Human Pose Estimation and Kinematic Validation in Athletic Movements

https://github.com/camma-public/hpe-adaptor

공식 코드

Unsupervised domain adaptation for clinician pose estimation and instance segmentation in the operating room

https://github.com/camma-public/selfpose3d

공식 코드

SelfPose3d: Self-Supervised Multi-Person Multi-View 3d Pose Estimation

https://github.com/cantarinigiorgio/HHP-Net

공식 코드

HHP-Net: A light Heteroscedastic neural network for Head Pose estimation with uncertainty

https://github.com/caojiezhang/VSR-Transformer

공식 코드

Video Super-Resolution Transformer

https://github.com/caojiezhang/davsr

공식 코드

Towards Interpretable Video Super-Resolution via Alternating Optimization

https://github.com/carluerjb/3d_sift_cuda

공식 코드

GPU optimization of the 3D Scale-invariant Feature Transform Algorithm and a Novel BRIEF-inspired 3D Fast Descriptor

https://github.com/carmenqing/vst-pose

공식 코드

VST-Pose: A Velocity-Integrated Spatiotem-poral Attention Network for Human WiFi Pose Estimation

https://github.com/casia-iva-lab/obj2seq

공식 코드

Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks

https://github.com/cavalli1234/nefsac

공식 코드

NeFSAC: Neurally Filtered Minimal Samples

https://github.com/cbsropenproject/6dof_face

공식 코드

Towards 3D Face Reconstruction in Perspective Projection: Estimating 6DoF Face Pose from Monocular Image

https://github.com/cedricrommel/manipose

공식 코드

ManiPose: Manifold-Constrained Multi-Hypothesis 3D Human Pose Estimation

https://github.com/ch-sa/labelCloud

공식 코드

labelCloud: A Lightweight Domain-Independent Labeling Tool for 3D Object Detection in Point Clouds

https://github.com/chaimi2013/CCM

공식 코드

Towards High Performance Human Keypoint Detection

https://github.com/chaitanya100100/TailorNet

공식 코드

TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style

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