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.
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Singing Style Transfer Using Cycle-Consistent Boundary Equilibrium Generative Adversarial Networks
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https://github.com/JepsonWong/Text_Classification
https://github.com/Jia-Research-Lab/Simple-SR
MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution
https://github.com/JiaquanYe/TableMASTER-mmocr
https://github.com/JimmyDoan1309/Neural-Style-Transfer
https://github.com/Jitensid/Neural-Style-Transfer
https://github.com/Jittor/InstanceSegmentation-jittor
https://github.com/Jmkernes/PAR-Transformer-XL
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https://github.com/JohanYe/CycleGAN-Pokemon
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/JohanZYe/CycleGAN-Pokemon
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/JohnGiorgi/compact-multi-head-self-attention-pytorch
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https://github.com/JonathanLehner/TecoGan-downloader
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Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
https://github.com/Josien94/MLiP
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Joyce511/OpenVino_Realtime_Multi-Person_Pose_Estimation
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/JudeLee19/HMNet
A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining
https://github.com/JudeLee19/HMNet-End-to-End-Abstractive-Summarization-for-Meetings
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https://github.com/JunkyByte/easy_ViTPose
ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation
https://github.com/JustWon/mystyletransfer
https://github.com/Justin-Tan/generative-compression
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Jwrede/neural_style_transfer
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
https://github.com/KPIxLILU/Machine-Learning-Workshop
https://github.com/Kaif10/Pose-Detection
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/KaiyangZhou/ssdg-benchmark
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
https://github.com/Kalyan0821/NST
https://github.com/Kalyan0821/myelinfoundry-challenge
https://github.com/KamilCendrowicz/NST
https://github.com/KamitaniLab/icnn
https://github.com/Kanaderu/nlp_credibility
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https://github.com/KangDo96/Github-Ranking
https://github.com/Karan-kapadia/Style-Transfer-karan
https://github.com/KarinaKorsgaard/Bartender-AI-OpenPose-Osc-Sender
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.