https://github.com/kai422/iart
Enhancing Video Super-Resolution via Implicit Resampling-based Alignment
Papers with Code 아카이브
3D Human Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 9
- 평가표
- 25
- 보존된 평가 기록
- 447
3D Human Pose Estimation is a computer vision task that involves estimating the 3D positions and orientations of body joints and bones from 2D images or videos. The goal is to reconstruct the 3D pose of a person in real-time, which can be used in a variety of applications, such as virtual reality, human-computer interaction, and motion analysis.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/karanshahgithub/CS256-AI-Pose2Mesh
Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose
https://github.com/karfly/learnable-triangulation-pytorch
https://github.com/karshetty/pliks
PLIKS: A Pseudo-Linear Inverse Kinematic Solver for 3D Human Body Estimation
https://github.com/kaulquappe23/a2b_human_mesh
Uplift and Upsample: Efficient 3D Human Pose Estimation with Uplifting Transformers
https://github.com/kdha0727/cancer-instance-segmentation-from-tissue
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
https://github.com/ken724049/action-recognition
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/kennymckormick/pyskl
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/khushgrover/smart-match
https://github.com/khyeyoon/Video-Super-Resolution
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
https://github.com/kingcheng2000/GAN
https://github.com/kingcong/RefineNet
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
https://github.com/kingcong/gpu_ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/kingcong/gpu_RefineNet
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
https://github.com/koi953215/NaRCan
Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution
https://github.com/kornia/kornia
https://github.com/kunjan-mhaske/Image-super-resolution-model-using-4-layers-CNN-from-PyTorch
https://github.com/kwantommy/breast-cancer-diagnosis
https://github.com/l13025816/PGCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/l3th4l/SuperRes
https://github.com/laobaiswag/openpose1
https://github.com/laomao0/BIN
https://github.com/laowang666888/HRNET
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/laowng/GISR
https://github.com/lee-aaron/Image-Super-Resolution
https://github.com/leeyegy/SimDR
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/leeyegy/simcc
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/leftthomas/espcn
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/moganet
https://github.com/liang-faan/openpose
https://github.com/ligaoqi2/res-loglikelihood-regression-with-COCO-video-demo
Human Pose Regression with Residual Log-likelihood Estimation
https://github.com/limaosen0/DMGNN
Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction
https://github.com/limi44/Parkinson-s-Pose-Estimation-Dataset
https://github.com/linxi159/GAN-training-tricks
https://github.com/linxi159/Tips-and-tricks-to-train-GANs
https://github.com/liuzhen03/ADNet
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.