2D Human Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 6
- 평가표
- 10
- 보존된 평가 기록
- 47
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/Gowti-AiboT/GAN-based-SISR-Keras
https://github.com/GuillaumeAI/rwml__adaptive_style_transfer
https://github.com/GuillaumeDufau/Image-super-resolution-vdsr
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
https://github.com/GuoShi28/Caffe-Instance-Normalization-and-Guided-Instance-Normalization
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
https://github.com/HERMINDERSINGH1234/ML_Extra_Resolution_Increases
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/HKUST-KnowComp/MnemonicReader
https://github.com/HKUST-KnowComp/R-Net
https://github.com/HRNet/HRNet-Facial-Landmark-Detection
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/HRNet/HRNet-Human-Pose-Estimation
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/HRNet/HRNet-Image-Classification
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/HRNet/HRNet-MaskRCNN-Benchmark
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/HRNet/HRNet-Object-Detection
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/HRNet/HRNet-Semantic-Segmentation
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/HarmanDotpy/Normalizations-in-Deep-Learning
Instance Normalization: The Missing Ingredient for Fast Stylization
https://github.com/Harrypotterrrr/EDVR
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
https://github.com/HazemLahoual/Project-Machine-Learning
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Hem7513/Decision-Trees-and-XGBoost-Algorithm-Documentation
https://github.com/HighVoltageRocknRoll/sr
https://github.com/HikkaV/StylizerML
Exploring the structure of a real-time, arbitrary neural artistic stylization network
https://github.com/HritwikEkade/Image-Resolution
https://github.com/HuikaiShao/JPFA
A Joint Pixel and Feature Alignment Framework for Cross-dataset Palmprint Recognition
https://github.com/HyeongJu916/Boaz-SR-ESRGAN-PyTorch
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/IShengFang/ishengfang
Self-Contained Stylization via Steganography for Reverse and Serial Style Transfer
https://github.com/Iamnotabear/Neural-Style-Transfer
https://github.com/IdanAzuri/pytorch_fewshot_with_deep_photo
https://github.com/Ign0reLee/EMD_Neural_Style_Transfer_with_Tensorflow
A Unified Framework for Generalizable Style Transfer: Style and Content Separation
https://github.com/IndigoAI/FontGenerator
https://github.com/Intelligent-Imaging-Center/Spectral-Reconstruction
https://github.com/IpastorSan/neural_style_transfer
https://github.com/J3698/AdaIN-reimplementation
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
https://github.com/JL321/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/JamesJang26/YALENLP-PerAnsSumm-2025
https://github.com/JamesLiao714/MC_GAN
https://github.com/JaryV/CycleGAN_OldYoung
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/JasperNg/ColorPreservationYUV
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.