https://github.com/zhy1001/Fast_Style_Transfer_using_CNN
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Learning Interpretable Representation for Controllable Polyphonic Music Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.