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.
https://github.com/rkem1542/EDSR-pytorch
https://github.com/rnwzd/FSPBT-Image-Translation
Interactive Video Stylization Using Few-Shot Patch-Based Training
https://github.com/roberttwomey/machine-imagination-isea
https://github.com/roberttwomey/machine-imagination-workshop
https://github.com/roimehrez/photorealism
Photorealistic Style Transfer with Screened Poisson Equation
https://github.com/rongjiehuang/transpeech
GenerSpeech: Towards Style Transfer for Generalizable Out-Of-Domain Text-to-Speech
https://github.com/rozumden/ShapeFromBlur
https://github.com/rozumden/tbd
https://github.com/rozumden/tbd3d
https://github.com/rpryzant/delete_retrieve_generate
Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer
https://github.com/rrmina/fast-neural-style-pytorch
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/rrmina/neural-style-pytorch
https://github.com/rrrepsac/tb_vc
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/rst-workbench/rst-workbench
https://github.com/ruc-nlpir/flashrag
Compressing Context to Enhance Inference Efficiency of Large Language Models
https://github.com/ruer98/headshot-style-transfer
https://github.com/rungjoo/Stable-Style-Transformer
Stable Style Transformer: Delete and Generate Approach with Encoder-Decoder for Text Style Transfer
https://github.com/ryanchankh/style_transfer
https://github.com/ryanhalabi/starcraft_super_resolution
https://github.com/ryanwebster90/image-synthesis-lab2
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
https://github.com/ryanwebster90/texture-synthesis-lab
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
https://github.com/ryersonvisionlab/two-stream-dyntex-synth
Two-Stream Convolutional Networks for Dynamic Texture Synthesis
https://github.com/rzhangpku/MFAE
https://github.com/s-nlp/sgdd-tst
Studying the role of named entities for content preservation in text style transfer
https://github.com/saadbutt32/Conversion-of-Pakistan-Sign-Languag-into-Text-and-Speech-using-OpenPose-and-Machine-Learning
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/saadbutt32/Pakistan-Sign-Language-Interpreter
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/sachin17git/iTransfer
https://github.com/sachin17git/iTransfer-Neural-Style-Transfer
https://github.com/sachin19/mucoco
Controlled Text Generation as Continuous Optimization with Multiple Constraints
https://github.com/sachit-n/drqa_doc_reader
https://github.com/safwankdb/Neural-Style-Transfer
https://github.com/safwankdb/ReCoNet-PyTorch
https://github.com/saihiel/news_headlines
Educating Text Autoencoders: Latent Representation Guidance via Denoising
https://github.com/saikatbsk/Vincent-AI-Artist
https://github.com/sainikhit2k/Art-Generation-using-Neural-Style-Transfer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.