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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/NVlabs/PAMTRI

공식 코드

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

https://github.com/NVlabs/ParallelInversion

공식 코드

Parallel Inversion of Neural Radiance Fields for Robust Pose Estimation

https://github.com/NVlabs/PoseRBPF

공식 코드

PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking

https://github.com/NVlabs/contact_graspnet

공식 코드

Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes

https://github.com/NVlabs/dex-ycb-toolkit

공식 코드

DexYCB: A Benchmark for Capturing Hand Grasping of Objects

https://github.com/NYXFLOWER/TIP

공식 코드

Tri-graph Information Propagation for Polypharmacy Side Effect Prediction

https://github.com/Nat-D/GMVAE

공식 코드

Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders

https://github.com/Nicholasli1995/EgoNet

공식 코드

Exploring intermediate representation for monocular vehicle pose estimation

https://github.com/Nicholasli1995/EvoSkeleton

공식 코드

Cascaded deep monocular 3D human pose estimation with evolutionary training data

https://github.com/NikolaZubic/2dimageto3dmodel

공식 코드

An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering

https://github.com/Nithin-GK/UniteandConquer

공식 코드

Unite and Conquer: Plug & Play Multi-Modal Synthesis using Diffusion Models

https://github.com/Oliverbansk/Hollistic-Robot-Pose-Estimation

공식 코드

Real-time Holistic Robot Pose Estimation with Unknown States

https://github.com/One-2-3-45/One-2-3-45

공식 코드

One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization

https://github.com/Open-Catalyst-Project/Open-Catalyst-Dataset

공식 코드

The Open Catalyst 2020 (OC20) Dataset and Community Challenges

https://github.com/Open-Catalyst-Project/ocp

공식 코드

The Open Catalyst 2020 (OC20) Dataset and Community Challenges

https://github.com/Owen-Liuyuxuan/visualDet3D

공식 코드

Ground-aware Monocular 3D Object Detection for Autonomous Driving

https://github.com/PKU-EPIC/GAPartNet

공식 코드

GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts

https://github.com/PKU-ICST-MIPL/FinePOSE_CVPR2024

공식 코드

FinePOSE: Fine-Grained Prompt-Driven 3D Human Pose Estimation via Diffusion Models

https://github.com/PStelldinger/PuzzleBoard

공식 코드

PuzzleBoard: A New Camera Calibration Pattern with Position Encoding

https://github.com/PaddlePaddle/PaddleGAN

공식 코드

PP-MSVSR: Multi-Stage Video Super-Resolution

https://github.com/PantoMatrix/PantoMatrix

공식 코드

EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture Modeling

https://github.com/ParitoshParmar/Fitness-AQA

공식 코드

Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment

https://github.com/PaulSK98/Nerf2Dataset

공식 코드

Automating 3D Dataset Generation with Neural Radiance Fields

https://github.com/PinataFarms/DAD-3DHeads

공식 코드

DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image

https://github.com/Pose-Group/DCPose

공식 코드

Deep Dual Consecutive Network for Human Pose Estimation

https://github.com/Project-Splinter/MonoPort

공식 코드

Monocular Real-Time Volumetric Performance Capture

https://github.com/PruneTruong/DenseMatching

공식 코드

Learning Accurate Dense Correspondences and When to Trust Them

https://github.com/PruneTruong/GLAMpoints_pytorch

공식 코드

GLAMpoints: Greedily Learned Accurate Match points

https://github.com/PruneTruong/PDCNet

공식 코드

Learning Accurate Dense Correspondences and When to Trust Them

https://github.com/Qi2019KB/UBPL-PoseEstimation

공식 코드

Generating Unbiased Pseudo-labels via a Theoretically Guaranteed Chebyshev Constraint to Unify Semi-supervised Classification and Regression

https://github.com/QianliM/CAPE

공식 코드

Learning to Dress 3D People in Generative Clothing

https://github.com/RahulSajnani/DRACO-Weakly-Supervised-Dense-Reconstruction-And-Canonicalization-of-Objects

공식 코드

DRACO: Weakly Supervised Dense Reconstruction And Canonicalization of Objects

https://github.com/Raojiyong/KITPose

공식 코드

Learning Structure-Supporting Dependencies via Keypoint Interactive Transformer for General Mammal Pose Estimation

https://github.com/SPICExLAB/MobilePoser

공식 코드

MobilePoser: Real-Time Full-Body Pose Estimation and 3D Human Translation from IMUs in Mobile Consumer Devices

https://github.com/SR42-dev/human-pose-quaternion-extraction

공식 코드

Economical Quaternion Extraction from a Human Skeletal Pose Estimate using 2-D Cameras

https://github.com/SUTDCV/Animal-Kingdom

공식 코드

Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding

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