https://github.com/2023-MindSpore-1/ms-code-216/tree/main/rbpn
Recurrent Back-Projection Network for Video Super-Resolution
Papers with Code 아카이브
3D Human Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 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/2023-MindSpore-1/ms-code-27
https://github.com/2023-MindSpore-1/ms-code-6/tree/main/CLIFF
CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation
https://github.com/2023-MindSpore-4/Code-5/tree/main/HMR
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
https://github.com/2023-MindSpore-4/Code8/tree/main/CLIFF
CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/graphormer
https://github.com/2024-MindSpore-1/Code7/tree/main/CLIFF
CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation
https://github.com/2xic/openpose-pigs
Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB
https://github.com/360jinrong/GBST
https://github.com/3dtopia/openlrm
https://github.com/545641826/espcn
https://github.com/AP-EPFL/DA-segmentation-driven-pose
Estimating 6D Pose From Localizing Designated Surface Keypoints
https://github.com/ARMUGHAN-SHAHID/MoboDensepose
https://github.com/Aanisha/SuperResolution
https://github.com/AbhinayReddyYarva/SuperResolution
https://github.com/AbiterVX/ST-GCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/AkashGanesan/PedestrianAttention
https://github.com/Alioth2000/RASVSR
https://github.com/AlongRide/Py3torch_HigherHRNet
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
https://github.com/Amritha16/ImageResolutionEnhancement
https://github.com/AniketVashishtha/SUPER_RESOLUTION_DL
https://github.com/AnjanaGJoseph/Super-Resolution-GAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/Araxeus/PNG-Upscale
https://github.com/Arthur151/CenterHMR
TRACE: 5D Temporal Regression of Avatars with Dynamic Cameras in 3D Environments
https://github.com/Automunge/AutoMunge
https://github.com/BIOINSu/LGFN
https://github.com/BearNinja123/esrGAN_vBearNinja123
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/CASIA-IVA-Lab/ISP-reID
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/CMU-Perceptual-Computing-Lab/MonocularTotalCapture
Monocular Total Capture: Posing Face, Body, and Hands in the Wild
https://github.com/CMU-Perceptual-Computing-Lab/openpose
https://github.com/CMU-Perceptual-Computing-Lab/openpose_unity_plugin
https://github.com/ClePol/CPM_Presentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.