https://github.com/faheinrich/moon_pose_estimation_setup
Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image
Papers with Code 아카이브
3D Human Pose Estimation
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- 평가표
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- 보존된 평가 기록
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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/faheinrich/moon_pose_with_detectron2
Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image
https://github.com/fcr3/OpenVINO_Tutorials
https://github.com/fcr3/wine_detector
https://github.com/fenghansen/ESRGAN-Keras
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/fengwang/subpixel_conv2d
https://github.com/fengyidong123/openpose-master
https://github.com/ferseiti/reproducibility
https://github.com/final-0/Real-ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/final-0/Real-ESRGAN-bicubic
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/fourseaforfriend/waifu2x
https://github.com/freedombenLiu/DensePose
https://github.com/gchb2012/VQA
Let's Transfer Transformations of Shared Semantic Representations
https://github.com/geaxgx/depthai_blazepose
https://github.com/geopavlakos/TexturePose
TexturePose: Supervising Human Mesh Estimation with Texture Consistency
https://github.com/geopavlakos/c2f-vol-demo
Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose
https://github.com/geopavlakos/c2f-vol-train
Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose
https://github.com/geopavlakos/ordinal-pose3d
https://github.com/george-gca/sr-pytorch-lightning
https://github.com/github-zbx/ST-GCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/gongzix/neuroclips
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
https://github.com/goutern/PoseEstimation
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/gox-ai/hrnet-pose-api
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/gq-sonofgod/zoom-picture
https://github.com/gs18113/ESPCN-TensorFlow2
https://github.com/gulvarol/bodynet
https://github.com/gulvarol/surreal
https://github.com/h44rd/PoseTransferMayaPlugin
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
https://github.com/halimasoulb/human-pose-estimation-3d-lidar
https://github.com/happyvictor008/High-order-GNN-LF-iter
A simple yet effective baseline for 3d human pose estimation
https://github.com/hariharannatesh/Action-Recognition-using-Pytorch-Geometric
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/harshalsonioo1/hpe
https://github.com/heartyguy/AI-AngryBird-Eagle-Wing
https://github.com/hiyaroy12/multitask_learning
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
https://github.com/hrhodin/UnsupervisedGeometryAwareRepresentationLearning
Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation
https://github.com/hugozanini/realtime-semantic-segmentation
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.