Papers with Code 아카이브
3D Hand Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 7
- 보존된 평가 기록
- 97
Image: Zimmerman et l
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/facebookresearch/assemblyhands-toolkit
AssemblyHands: Towards Egocentric Activity Understanding via 3D Hand Pose Estimation
https://github.com/geopavlakos/hamer
https://github.com/grgward108/PosePretrain
A Unified Transformer-Based Framework with Pretraining For Whole Body Grasping Motion Generation
https://github.com/guiggh/hand_pose_action
First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations
https://github.com/hanchenchen/hand-complete-w-Handtailor
HandTailor: Towards High-Precision Monocular 3D Hand Recovery
https://github.com/hassony2/manopth
Learning joint reconstruction of hands and manipulated objects
https://github.com/hassony2/obman
Learning joint reconstruction of hands and manipulated objects
https://github.com/hassony2/obman_train
Learning joint reconstruction of hands and manipulated objects
https://github.com/hsp-iit/byogg
Bring Your Own Grasp Generator: Leveraging Robot Grasp Generation for Prosthetic Grasping
https://github.com/hxwork/H2ONet_Pytorch
https://github.com/jsll/pytorch_6dof-graspnet
6-DOF GraspNet: Variational Grasp Generation for Object Manipulation
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/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/fastvit
FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization
https://github.com/lzfff12/hfl-net
https://github.com/nanzhaogang/contrib/tree/master/application/weakly-supervised-mesh-convolutional-hand-reconstruction-in-the-wild
Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild
https://github.com/nkolot/GraphCMR
Convolutional Mesh Regression for Single-Image Human Shape Reconstruction
https://github.com/open-mmlab/mmpose
https://github.com/otaheri/GrabNet
https://github.com/patiencefromzhou/simplehand
https://github.com/poier/murauer
https://github.com/pwc-1/Paper-9/tree/main/4/weakly-supervised
Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild
https://github.com/rajbharat/PoseNet-V2V-Pytorch1.0-Win10
https://github.com/russoale/hmr2.0
https://github.com/shreyashampali/HOnnotate
HOnnotate: A method for 3D Annotation of Hand and Object Poses
https://github.com/shreyashampali/ho3d
HOnnotate: A method for 3D Annotation of Hand and Object Poses
https://github.com/theerapatkitti/hand_mask_rcnn
https://github.com/theone1984/hand-gesture-ai
https://github.com/ut-vision/actionvos
Single-to-Dual-View Adaptation for Egocentric 3D Hand Pose Estimation
https://github.com/wozhangzhaohui/HandAugment
HandAugment: A Simple Data Augmentation Method for Depth-Based 3D Hand Pose Estimation
https://github.com/xiuyu0000/new_papers_codes/tree/main/cmr
Convolutional Mesh Regression for Single-Image Human Shape Reconstruction
https://github.com/yusukey03012/deformer
https://github.com/zerchen/AlignSDF
Grasping Field: Learning Implicit Representations for Human Grasps
https://github.com/zerchen/gSDF
Grasping Field: Learning Implicit Representations for Human Grasps
https://github.com/zhong110020/hand3d
https://github.com/zhouxian/FluidLab
Diffusion-based Generation, Optimization, and Planning in 3D Scenes
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.