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/michael13162/DoodleGAN
https://github.com/michel-liu/grouppose
Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation
https://github.com/michel-liu/grouppose-paddle
Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation
https://github.com/mickan123/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/microsoft/art
MosAIc: Finding Artistic Connections across Culture with Conditional Image Retrieval
https://github.com/microsoft/msrflute
https://github.com/microsoft/speedyrec
Training Large-Scale News Recommenders with Pretrained Language Models in the Loop
https://github.com/mid-push/irwgan
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/midm/FastPhotoStyle
https://github.com/milmor/GPT
https://github.com/milmor/perceptual-losses-neural-style
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/mindspore-ai/contrib/tree/master/intern/pixelshuffle1d
https://github.com/mindspore-ai/models/blob/master/research/cv/simple_baselines/
https://github.com/mindspore-ai/models/tree/master/official/cv/openpose
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/mindspore-ai/models/tree/master/official/cv/simple_pose
https://github.com/mindspore-ai/models/tree/master/official/cv/srcnn
https://github.com/mindspore-ai/models/tree/master/official/nlp/gpt
https://github.com/mindspore-ai/models/tree/master/research/cv/AlphaPose
https://github.com/mindspore-ai/models/tree/master/research/cv/ArbitraryStyleTransfer
Exploring the structure of a real-time, arbitrary neural artistic stylization network
https://github.com/mindspore-ai/models/tree/master/research/cv/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/mindspore-ai/models/tree/master/research/cv/ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/mindspore-ai/models/tree/master/research/cv/fairmot
https://github.com/mindspore-courses/heads-on-mindspore/tree/main/3-neural_style
https://github.com/mindspore-lab/mindone
https://github.com/mindspore-lab/mindone/blob/master/examples/stable_diffusion_v2/T2I-Adapter.md
https://github.com/minhmanho/deep_preset
Deep Preset: Blending and Retouching Photos with Color Style Transfer
https://github.com/mirandrom/HipoRank
Discourse-Aware Unsupervised Summarization of Long Scientific Documents
https://github.com/misuy/misuy_style_transfer_bot
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/mithun-bharadwaj/Neural_Style_Transfer
https://github.com/mks0601/TF-SimpleHumanPose
https://github.com/mlh-fellowship/neuro-art
https://github.com/mlvc-lab/CycleGan_pytorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/mmakiuchi/multimodal_emotion_recognition
AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
https://github.com/mmcux/de-nds-translation
WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia
https://github.com/mnicnc404/CartoonGan-tensorflow
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.