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/archie252000/NeuralStyleTransfer
https://github.com/arjunarao619/SRCNN_Pytorch
https://github.com/arnab39/cycleGAN-PyTorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/arnavdodiedo/Neural-Style-Transfer
https://github.com/arnavdodiedo/dog2sketch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/arnavdodiedo/sketch2face
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/arne-cl/rst-workbench
https://github.com/arthur-qiu/freenoise-lavie
LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models
https://github.com/arthurbra/stm-corpus/blob/master/README.md
Domain-independent Extraction of Scientific Concepts from Research Articles
https://github.com/artitw/BERT_QA
https://github.com/arvind1998/Neural-Style-Transfer
https://github.com/aryan-mann/style-transfer
https://github.com/aryan26roy/Neural-Style-Transfer
https://github.com/asahi417/lmppl
https://github.com/asahi417/relbert
https://github.com/aseidelo/wiki_generator
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Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/asraman9792/Generative-Adversarial-Networks-implementation-of-KERAS
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/asraman9792/Image-Dehazing-GANS
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/assafelovic/gpt-researcher
Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models
https://github.com/asyml/texar/tree/master/examples/text_style_transfer
Unsupervised Text Style Transfer using Language Models as Discriminators
https://github.com/atharva-18/Neural-Style-Transfer
https://github.com/atheesh1998/Image-Super-Resolution
https://github.com/atriumlts/subpixel
https://github.com/atulkumarin/DeClare
DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning
https://github.com/aurelien-peden/Deep-Learning-paper-implementations
https://github.com/auspicious3000/autopst
https://github.com/auspicious3000/autovc
AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
https://github.com/avellal14/AI-for-Art
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/avshalomc/subtract
https://github.com/awant/arae
https://github.com/axinc-ai/ailia-models/tree/master/pose_estimation_3d/blazepose-fullbody
https://github.com/ayanglab/swinganmr
https://github.com/ayanglab/swinmr
https://github.com/ayush714/Covid-19-Fake-News-Detection-System-
Transformer based Automatic COVID-19 Fake News Detection System
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.