https://github.com/soumik12345/Pix2Pix
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
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.
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Associative Embedding: End-to-End Learning for Joint Detection and Grouping
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Deep High-Resolution Representation Learning for Human Pose Estimation
WangchanBERTa: Pretraining transformer-based Thai Language Models
Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
Instance Normalization: The Missing Ingredient for Fast Stylization
Learning Linear Transformations for Fast Arbitrary Style Transfer
MUSE: Machine Unlearning Six-Way Evaluation for Language Models
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?
Neural Painters: A learned differentiable constraint for generating brushstroke paintings
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.