https://github.com/LucasSheng/avatar-net
Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration
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.
Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization
Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization
PPT: token-Pruned Pose Transformer for monocular and multi-view human pose estimation
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
Style Transfer by Rigid Alignment in Neural Net Feature Space
Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies
Instance Normalization: The Missing Ingredient for Fast Stylization
Deep High-Resolution Representation Learning for Human Pose Estimation
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Science Concierge: A fast content-based recommendation system for scientific publications
Recurrent Back-Projection Network for Video Super-Resolution
Exploring the structure of a real-time, arbitrary neural artistic stylization network
Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation
Recurrent Back-Projection Network for Video Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.