Papers with Code 아카이브

3D Absolute Human Pose Estimation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 4
평가표: 3
보존된 평가 기록: 6

This task aims to solve absolute (camera-centric not root-relative) 3D human pose estimation.

<span style="color:grey; opacity: 0.6">( Image credit: RootNet )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/open-mmlab/mmsr

EDVR: Video Restoration with Enhanced Deformable Convolutional Networks

https://github.com/osofr/xgboost

XGBoost: A Scalable Tree Boosting System

https://github.com/otaheri/GOAL

ICON: Implicit Clothed humans Obtained from Normals

https://github.com/peteryuX/esrgan-tf2

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/pierobeat/Hoax-News-Classification

XGBoost: A Scalable Tree Boosting System

https://github.com/pilot7747/sldl

Designing a Practical Degradation Model for Deep Blind Image Super-Resolution

https://github.com/pohwa065/Deep-Learning-for-Super-Resolution-and-Image-Enhancement

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/pohwa065/SRGAN-for-Super-Resolution-and-Image-Enhancement

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/poikilos/pyrotocanvas

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/poyushen/classifaction

XGBoost: A Scalable Tree Boosting System

https://github.com/psychopa4/PFNL

https://github.com/pwc-1/Paper-9/tree/main/4/pixelshuffle1d

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/pwc-1/Paper-9/tree/main/7/pixelshuffle1d

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/quin-med-harvard-edu/kidney

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/r06922019/butt_lion_paper_notes

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/rajatthepagal/DBPN-Keras

Deep Back-Projection Networks For Super-Resolution

https://github.com/rami0205/ngramswin

SwinIR: Image Restoration Using Swin Transformer

https://github.com/renyang-home/ntire22_venh_sr

BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment

https://github.com/rharish101/DeFMO

DeFMO: Deblurring and Shape Recovery of Fast Moving Objects

https://github.com/rkem1542/EDSR-pytorch

Deep Back-Projection Networks For Super-Resolution

https://github.com/rozumden/ShapeFromBlur

DeFMO: Deblurring and Shape Recovery of Fast Moving Objects

https://github.com/rozumden/tbd

Intra-frame Object Tracking by Deblatting

https://github.com/rozumden/tbd3d

Intra-frame Object Tracking by Deblatting

https://github.com/russoale/hmr2.0

End-to-end Recovery of Human Shape and Pose

https://github.com/ryanhalabi/starcraft_super_resolution

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/sanghyun-son/EDSR-PyTorch

Deep Back-Projection Networks For Super-Resolution

https://github.com/sap3d/Sap3D

The More You See in 2D, the More You Perceive in 3D

https://github.com/sberbank-ai/DigiTales

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/sberbank-ai/DigiTeller

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/sberbank-ai/real-esrgan

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/sczhou/upscale-a-video

Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution

https://github.com/sdauzcm/sr-basicsr

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/sdxone/resolution

Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation

https://github.com/sgrvinod/a-pytorch-tutorial-to-super-resolution

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/shreeyashyende/better_img_res_with_SRCNN

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/skchen1993/SwinIR

SwinIR: Image Restoration Using Swin Transformer

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.