Papers with Code 아카이브

3D Face Animation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

1

평가표

3

보존된 평가 기록

12

코드

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

https://github.com/tegg89/SRCNN-Tensorflow

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/tetrachrome/subpixel

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

https://github.com/thstkdgus35/EDSR-PyTorch

Deep Back-Projection Networks For Super-Resolution

https://github.com/tinnunculus/Denoising_SR

Deep Back-Projection Networks For Super-Resolution

https://github.com/titu1994/Image-Super-Resolution

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/tom-doerr/TecoGAN

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

https://github.com/tom-doerr/TecoGAN-Docker

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

https://github.com/tqchen/xgboost

XGBoost: A Scalable Tree Boosting System

https://github.com/twhui/SRGAN-PyTorch

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

https://github.com/tyshiwo/DRRN_CVPR17

https://github.com/u7javed/Image-Enhancer-via-ESRGAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/u7javed/Image-Super-Resolution-Enhancer-via-ESRGAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/vpaliwal1/Deep_learning_SRCNN

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/vscoboy/AI-

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

https://github.com/vuanhtu1993/Keras-SRGANs

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

https://github.com/wh1h/DNLN

Deformable Non-local Network for Video Super-Resolution

https://github.com/why986/VFA

MMFace4D: A Large-Scale Multi-Modal 4D Face Dataset for Audio-Driven 3D Face Animation

https://github.com/wustl-cig/FLAIR

FLAIR: A Conditional Diffusion Framework with Applications to Face Video Restoration

https://github.com/wwhio/megmodels

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/xg416/DATUM

Recurrent Video Restoration Transformer with Guided Deformable Attention

https://github.com/xgd/waifu2xx

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/xiadanqing/Binary

XGBoost: A Scalable Tree Boosting System

https://github.com/xinntao/BasicSR

BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

https://github.com/xiuyu0000/new_papers_codes/tree/main/rbpn

Recurrent Back-Projection Network for Video Super-Resolution

https://github.com/xoraus/Super-Resolution-CNN-for-Image-Restoration

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/yangyucheng000/ESRGAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/yfeng95/deca

Learning an Animatable Detailed 3D Face Model from In-The-Wild Images

https://github.com/yhjo09/VSR-DUF

https://github.com/yhw-yhw/show

Generating Holistic 3D Human Motion from Speech

https://github.com/yjn870/srcnn-pytorch

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/yslenjoy/machine_learning_SuperResolution

Accurate Image Super-Resolution Using Very Deep Convolutional Networks

https://github.com/zhangtianmingxp/rbpn_mindspore

Recurrent Back-Projection Network for Video Super-Resolution

https://github.com/zhenglinzhou/headstudio

Generating Holistic 3D Human Motion from Speech

https://github.com/zhusiling/EDSR

Deep Back-Projection Networks For Super-Resolution

https://github.com/zhusiling/EDVR

EDVR: Video Restoration with Enhanced Deformable Convolutional Networks

https://github.com/zhusiling/TecoGAN

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

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