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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https://github.com/code-implementation1/Code2/tree/main/ArbitraryStyleTransfer
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https://github.com/code-implementation1/Code3/tree/main/ESRGAN
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
https://github.com/code-implementation1/Code6/tree/main/ras
Recurrent Back-Projection Network for Video Super-Resolution
https://github.com/code-implementation1/Code8/tree/main/SinGAN
SinGAN-Seg: Synthetic training data generation for medical image segmentation
https://github.com/code-implementation1/Code8/tree/main/simple_baselines
https://github.com/code-implementation1/Code8/tree/main/simple_pose
Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation
https://github.com/code-implementation1/Code9/tree/main/EDSR
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
https://github.com/code-implementation1/Code9/tree/main/Efficientnet
EfficientHRNet: Efficient Scaling for Lightweight High-Resolution Multi-Person Pose Estimation
https://github.com/code-implementation1/Code9/tree/main/hrnet
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
https://github.com/codedotal/gpt-code-clippy
https://github.com/colemiller94/gatedgan
Gated-GAN: Adversarial Gated Networks for Multi-Collection Style Transfer
https://github.com/contextlab/abstract2paper
https://github.com/corcovadoming/mpnnel
https://github.com/coulsonlee/dy-dca-eccv2024
Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting
https://github.com/crazydigger/Callibration-of-GPT
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GAN-QP: A Novel GAN Framework without Gradient Vanishing and Lipschitz Constraint
https://github.com/cristinae/WikiTailor
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https://github.com/cristinecosta/CompVis
https://github.com/cryu854/ArbitraryStyle-tfjs
https://github.com/cryu854/FastStyle
https://github.com/cryu854/SinGAN
Instance Normalization: The Missing Ingredient for Fast Stylization
https://github.com/cscss/DBVSR
https://github.com/ctr4si/MMN
Abstractive Summarization of Reddit Posts with Multi-level Memory Networks
https://github.com/cvfx-2019/homework1-color-transfer
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/cviaai/PSF-GAN-ESTIMATION
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/cvp19g2/cvp19g2
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/cydessole/AnimeLife
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/cynicaldevil/neural-style-transfer
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
https://github.com/cysmith/neural-style-tf
https://github.com/d-shivam/Pose-estimation-based-action-recognition-for-help-Situation-Identification
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/daintlab/ct-denoising
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.