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/liuzeyuMr/CycleGAN-TensorFlow-master
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/liuzhen03/ADNet
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
https://github.com/liuzhengwei127/Styfer
https://github.com/lixilinx/psgd_tf
https://github.com/lixinghpu/SCCL
https://github.com/lizekang/ITDD
https://github.com/lizeng614/SqueezeNet-Neural-Style-Pytorch
https://github.com/lizhuoq/ESRGAN-pytorch
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/lllyasviel/style2paints
Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN
https://github.com/lmarent/TranslateAlignRetrieve
MLQA: Evaluating Cross-lingual Extractive Question Answering
https://github.com/lmn-extracts/dcn_plus
https://github.com/lncarter/Openpose
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/locuslab/open-unlearning
MUSE: Machine Unlearning Six-Way Evaluation for Language Models
https://github.com/longhao-chen/aicas2024
https://github.com/loris3/evaluation_explanation_quality
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
https://github.com/lornatang/cyclegan-pytorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/louis-li/wildtrack_footprint
https://github.com/louis-monier/recommendation_engine
A Contextual-Bandit Approach to Personalized News Article Recommendation
https://github.com/ls4gan/benchmarking
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/ltruncel/Microsoft_Azure_50daysofudacity
https://github.com/lu-m13/optimal
https://github.com/luanyi/DyGIE
https://github.com/lucasmfaria/Machine-Learning-Capstone
https://github.com/lucidrains/gated-state-spaces-pytorch
https://github.com/lucidrains/memory-compressed-attention
https://github.com/luciennnnnnn/dualformer
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/lukeandshuo/IR2VI_journal
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/lukeditria/cnn-vae
https://github.com/lvyufeng/bert4ms
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
https://github.com/lwxGitHub123/openpose
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/lwxGitHub123/openpose20200610
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/lxy5513/Multi-Style-Transfer
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
https://github.com/lyonling/pix2pix
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/lyqcom/alphapose
https://github.com/mahinanwar/Art-Generation-With-Neural-Style-Transfer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.