Papers with Code 아카이브

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/agnesdeng/mixgb

XGBoost: A Scalable Tree Boosting System

https://github.com/ahmedosman/STAR

VIBE: Video Inference for Human Body Pose and Shape Estimation

https://github.com/ai-forever/kandinskyvideo

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

https://github.com/aim-uofa/AdelaiDet

DirectPose: Direct End-to-End Multi-Person Pose Estimation

https://github.com/aim-uofa/adet

DirectPose: Direct End-to-End Multi-Person Pose Estimation

https://github.com/airalcorn2/aquamam

AQuaMaM: An Autoregressive, Quaternion Manifold Model for Rapidly Estimating Complex SO(3) Distributions

https://github.com/airalcorn2/pytorch-ipdf

Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold

https://github.com/ajda1992/convformer

ConvFormer: Parameter Reduction in Transformer Models for 3D Human Pose Estimation by Leveraging Dynamic Multi-Headed Convolutional Attention

https://github.com/ajdillhoff/colorhandpose3d-pytorch

Learning to Estimate 3D Hand Pose from Single RGB Images

https://github.com/ajdillhoff/simgan-pytorch

Learning from Simulated and Unsupervised Images through Adversarial Training

https://github.com/akanazawa/hmr

End-to-end Recovery of Human Shape and Pose

https://github.com/akanazawa/motion_reconstruction

SFV: Reinforcement Learning of Physical Skills from Videos

https://github.com/akashpalrecha/deblurring-pipeline

Deep Back-Projection Networks For Super-Resolution

https://github.com/akashpalrecha/superres-deformable

Deep Back-Projection Networks For Super-Resolution

https://github.com/alexalm4190/Mask_RCNN-Vizzy_Hand

https://github.com/alexander-pv/maskrcnn_tf2

https://github.com/alexandrejash/Chivvo

Neural Ordinary Differential Equations

https://github.com/alexjc/neural-enhance

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

https://github.com/aliabigdeli/nnenum

Verification of Deep Convolutional Neural Networks Using ImageStars

https://github.com/alibaba/EasyCV

Lite-HRNet: A Lightweight High-Resolution Network

https://github.com/alililia/ascend_ESRGAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/alililia/ascend_HRNetW48_cls

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/alililia/ascend_RefineNet

RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

https://github.com/alililia/ascend_maskrcnn_mobilenetv1

https://github.com/alililia/gpu_maskrcnn_mobilenetv1

https://github.com/aliyun/Self-Evolving-Keypoint-Demo

SEKD: Self-Evolving Keypoint Detection and Description

https://github.com/allenai/satlas-super-resolution

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/alterzero/DBPN-Pytorch

Deep Back-Projection Networks For Super-Resolution

https://github.com/alterzero/RBPN-PyTorch

Recurrent Back-Projection Network for Video Super-Resolution

https://github.com/alterzero/STARnet

Space-Time-Aware Multi-Resolution Video Enhancement

https://github.com/amakadia/svd_for_pose

An Analysis of SVD for Deep Rotation Estimation

https://github.com/amanshenoy/image-super-resolution

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/ambareeshsrja16/Surgical_Tool_Tracking

SuPer Deep: A Surgical Perception Framework for Robotic Tissue Manipulation using Deep Learning for Feature Extraction

https://github.com/amirgholami/anode

Neural Ordinary Differential Equations

https://github.com/amogh7joshi/media-vision

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/amzamzamzamz/nagadomi-waifu2x

Image Super-Resolution Using Deep Convolutional Networks

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