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/YvanG/VQGAN-CLIP

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/Zhangyanbo/iResNetLab

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

https://github.com/ZhaoHengrun/EDVR

EDVR: Video Restoration with Enhanced Deformable Convolutional Networks

https://github.com/ZiqiaoPeng/EmoTalk

EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation

https://github.com/aba450/Super-Resolution

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/abdulwaheedsoudagar/SR-CNN

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/adbobes/VideoSuperResolution

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

https://github.com/agnJason/RGB-D-PIFuHD

PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization

https://github.com/agnesdeng/mixgb

XGBoost: A Scalable Tree Boosting System

https://github.com/ai-forever/kandinskyvideo

Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models

https://github.com/akanazawa/hmr

End-to-end Recovery of Human Shape and Pose

https://github.com/akashpalrecha/deblurring-pipeline

Deep Back-Projection Networks For Super-Resolution

https://github.com/akashpalrecha/superres-deformable

Deep Back-Projection Networks For Super-Resolution

https://github.com/alexjc/neural-enhance

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

https://github.com/alililia/ascend_ESRGAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/alililia/ascend_RefineNet

RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

https://github.com/allenai/satlas-super-resolution

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/alterzero/DBPN-Pytorch

Deep Back-Projection Networks For Super-Resolution

https://github.com/alterzero/RBPN-PyTorch

Recurrent Back-Projection Network for Video Super-Resolution

https://github.com/alterzero/STARnet

Space-Time-Aware Multi-Resolution Video Enhancement

https://github.com/amanshenoy/image-super-resolution

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/amogh7joshi/media-vision

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/amzamzamzamz/nagadomi-waifu2x

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/anchen1011/toflow

Video Enhancement with Task-Oriented Flow

https://github.com/andreacoppari/ELSR-torch

ELSR: Extreme Low-Power Super Resolution Network For Mobile Devices

https://github.com/anilarmagan/HANDS19-Challenge-Toolbox

End-to-end Recovery of Human Shape and Pose

https://github.com/anktplwl91/Image-Superresolution

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

https://github.com/anujdutt9/ESPCN

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

https://github.com/arjunarao619/SRCNN_Pytorch

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/arthur-qiu/freenoise-lavie

LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models

https://github.com/atheesh1998/Image-Super-Resolution

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/atriumlts/subpixel

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

https://github.com/ayanglab/swinganmr

SwinIR: Image Restoration Using Swin Transformer

https://github.com/ayanglab/swinmr

SwinIR: Image Restoration Using Swin Transformer

https://github.com/bayuudachi/Real-esr-gan

Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

https://github.com/behnoudshafizadeh/ESPCN-SuperResolution-Tensorflow

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

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