2D Human Pose Estimation
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What is Human Pose Estimation? Human pose estimation is the process of estimating the configuration of the body (pose) from a single, typically monocular, image. Background. Human pose estimation is one of the key problems in computer vision that has been studied for well over 15 years. The reason for its importance is the abundance of applications that can benefit from such a technology. For example, human pose estimation allows for higher-level reasoning in the context of human-computer interaction and activity recognition; it is also one of the basic building blocks for marker-less motion capture (MoCap) technology. MoCap technology is useful for applications ranging from character animation to clinical analysis of gait pathologies.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/NVIDIA/mellotron
https://github.com/NVIDIA/radtts
Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis
https://github.com/NVlabs/PAMTRI
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/NamSahng/SingingStyleTransfer
Singing Style Transfer Using Cycle-Consistent Boundary Equilibrium Generative Adversarial Networks
https://github.com/Nanway/dfc-vae
https://github.com/NataliaSkorokhod/neural-style-transfer
https://github.com/NateKoenig/https-github.com-Kautenja-a-neural-algorithm-of-artistic-style
https://github.com/NathanDeMaria/AugmentedCycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Ne-ki-tos/Super_res
https://github.com/Neroxn/Style-Aware-Normalized/tree/main/Project_Kargi
Style-Aware Normalized Loss for Improving Arbitrary Style Transfer
https://github.com/Net-Mist/style-transfer-tf2
Instance Normalization: The Missing Ingredient for Fast Stylization
https://github.com/NeuralVFX/lighting-swap-cyclegan
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Nhat-Thanh/ESPCN-Pytorch
https://github.com/Nhat-Thanh/ESPCN-TF
https://github.com/Nhat-Thanh/SRCNN-Pytorch
https://github.com/Nhat-Thanh/SRCNN-TF
https://github.com/Nhat-Thanh/VDSR-Pytorch
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
https://github.com/NicicDamjan/SoftKompjuting
https://github.com/Nikhil619/Motion-direction-control-of-a-pi-robot-using-hand-movementss
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/Nirav-Agarwal/GANs
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/NitishaS-812k/Monet-CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/NiucunCode/Deep-Learning-proj2
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/NoMorningstar/style-transfer-1
https://github.com/OfekCohen1/Style-On-3D-Video
https://github.com/Oishani/Machine-Learning-Projects
https://github.com/Omkar-Ranadive/Domain-Adaptation-CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/One-sixth/gan-qp-mod-pytorch
GAN-QP: A Novel GAN Framework without Gradient Vanishing and Lipschitz Constraint
https://github.com/One-sixth/pixelshuffle_invert_pytorch
https://github.com/Our4514/CAGFUZZ
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/PARC/intricate-art-neural-transfer
https://github.com/PDEUXA/AIF_CYCLEGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/PKU-TANGENT/nlp-tutorial
https://github.com/PacktPublishing/Hands-On-Image-Generation-with-TensorFlow-2.0/tree/master/Chapter04
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/PacktPublishing/Hands-On-Image-Generation-with-TensorFlow-2.0/tree/master/Chapter05
https://github.com/PaddlePaddle/PaddleDetection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.