https://github.com/tahasamavati/Cycle-GAN-Tensorflow
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
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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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks
Image-to-Image Translation via Group-wise Deep Whitening-and-Coloring Transformation
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Instance Normalization: The Missing Ingredient for Fast Stylization
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Predicting Subjective Features of Questions of QA Websites using BERT
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Relatable Clothing: Detecting Visual Relationships between People and Clothing
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Instance Normalization: The Missing Ingredient for Fast Stylization
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Deep High-Resolution Representation Learning for Human Pose Estimation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.