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/zmzhang1998/Real-RawVSR

공식 코드

Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark Dataset

https://huggingface.co/datasets/sankalpsinha77/MARVEL-40M

공식 코드

MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content Creation

https://github.com/07Agarg/Image-Resolution-Enhancement-SRCNN

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/1082-datascience/finalproject-finalproject-1082ds_group3

XGBoost: A Scalable Tree Boosting System

https://github.com/15517965908/keras-DBPN

Deep Back-Projection Networks For Super-Resolution

https://github.com/20151001860/Real_ESRGAN_paddle

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

https://github.com/2023-MindSpore-1/ms-code-210/tree/main/DBPN

Deep Back-Projection Networks For Super-Resolution

https://github.com/2023-MindSpore-1/ms-code-213/tree/main/ESRGAN

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

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/RefineNet

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

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/rbpn

Recurrent Back-Projection Network for Video Super-Resolution

https://github.com/2023-MindSpore-1/ms-code-27

End-to-end Recovery of Human Shape and Pose

https://github.com/2023-MindSpore-4/Code-5/tree/main/HMR

End-to-end Recovery of Human Shape and Pose

https://github.com/2023-MindSpore-4/Code11/tree/main/SRGAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/2023-MindSpore-4/Code6/tree/main/RefineNet

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

https://github.com/2023-MindSpore-4/Code7/tree/main/SRGAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/2023-MindSpore-4/Code7/tree/main/srcnn

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/360jinrong/GBST

XGBoost: A Scalable Tree Boosting System

https://github.com/3dtopia/openlrm

LRM: Large Reconstruction Model for Single Image to 3D

https://github.com/545641826/espcn

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

https://github.com/Aanisha/SuperResolution

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/AbhinayReddyYarva/SuperResolution

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/Alioth2000/RASVSR

https://github.com/Amritha16/ImageResolutionEnhancement

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/AniketVashishtha/SUPER_RESOLUTION_DL

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/AnjanaGJoseph/Super-Resolution-GAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/Araxeus/PNG-Upscale

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

https://github.com/Automunge/AutoMunge

XGBoost: A Scalable Tree Boosting System

https://github.com/BIOINSu/LGFN

https://github.com/BearNinja123/esrGAN_vBearNinja123

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/Coldog2333/pytoflow

Video Enhancement with Task-Oriented Flow

https://github.com/CorentinMAG/SRGAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/Dictator890/ESRGAN-Android-Demo

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/DreamTechAI/Direct3D

Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion Transformer

https://github.com/DreamTechAI/Direct3D-S2

Direct3D-S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attention

https://github.com/DwightFoster/Pytorch-TecoGan

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

https://github.com/Feynman1999/MgeEditing

Video Super-Resolution with Recurrent Structure-Detail Network

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