https://github.com/peteryuX/esrgan-tf2
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
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/philipNoonan/OPVP3D
3D human pose estimation in video with temporal convolutions and semi-supervised training
https://github.com/pierobeat/Hoax-News-Classification
https://github.com/pilot7747/sldl
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
https://github.com/pixelite1201/CameraHMR
https://github.com/pminhtam/2D-3D_Multitask_Deep_Learning
2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning
https://github.com/pohwa065/Deep-Learning-for-Super-Resolution-and-Image-Enhancement
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/pohwa065/SRGAN-for-Super-Resolution-and-Image-Enhancement
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/poikilos/pyrotocanvas
https://github.com/poyushen/classifaction
https://github.com/psychopa4/PFNL
https://github.com/pwc-1/Paper-5/tree/main/mpt
MPT: Mesh Pre-Training with Transformers for Human Pose and Mesh Reconstruction
https://github.com/pwc-1/Paper-9/tree/main/2/mpt
MPT: Mesh Pre-Training with Transformers for Human Pose and Mesh Reconstruction
https://github.com/pwc-1/Paper-9/tree/main/4/pixelshuffle1d
https://github.com/pwc-1/Paper-9/tree/main/7/pixelshuffle1d
https://github.com/qdhill/openpose
https://github.com/qingyun-wu/autogen-eval
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
https://github.com/quin-med-harvard-edu/kidney
https://github.com/r-pad/taxpose
Deep SE(3)-Equivariant Geometric Reasoning for Precise Placement Tasks
https://github.com/r06922019/butt_lion_paper_notes
https://github.com/radioactiverockets/OpenPoseBreackoutGame
https://github.com/rajatthepagal/DBPN-Keras
https://github.com/rajbharat/PoseNet-V2V-Pytorch1.0-Win10
https://github.com/rami0205/ngramswin
https://github.com/rayat137/Pose_3D
https://github.com/raymondyeh07/chirality_nets
3D human pose estimation in video with temporal convolutions and semi-supervised training
https://github.com/redrock303/HEMlets
HEMlets PoSh: Learning Part-Centric Heatmap Triplets for 3D Human Pose and Shape Estimation
https://github.com/renyang-home/ntire22_venh_sr
BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment
https://github.com/revanurambareesh/multiperson
Unsupervised Cross-Modal Alignment for Multi-Person 3D Pose Estimation
https://github.com/rharish101/DeFMO
https://github.com/rkem1542/EDSR-pytorch
https://github.com/roboverseorg/roboverse
https://github.com/romanus/code_tekinetal_iccv17
Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation
https://github.com/rongakowang/densemutualattention
HOnnotate: A method for 3D Annotation of Hand and Object Poses
https://github.com/rozumden/ShapeFromBlur
https://github.com/rozumden/tbd
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.