https://github.com/DreamTechAI/Direct3D
Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion Transformer
Papers with Code 아카이브
Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 17
- 평가표
- 31
- 보존된 평가 기록
- 235
Pose Estimation is a computer vision task where the goal is to detect the position and orientation of a person or an object. Usually, this is done by predicting the location of specific keypoints like hands, head, elbows, etc. in case of Human Pose Estimation.
A common benchmark for this task is MPII Human Pose
<span style="color:grey; opacity: 0.6">( Image credit: Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/DreamTechAI/Direct3D-S2
Direct3D-S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attention
https://github.com/Droliven/pymaf_reimplementation
PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop
https://github.com/DwightFoster/Pytorch-TecoGan
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/EAST-J/Youtubehand
Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild
https://github.com/ECE740F21T01/pytorch-pose-hg-3d
Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
https://github.com/EMI-Group/TransferLearning-Tasks
RelativeNAS: Relative Neural Architecture Search via Slow-Fast Learning
https://github.com/EMalagoli92/VAN-Classification-TensorFlow
https://github.com/Easonyesheng/A2PM-MESA
https://github.com/EdoardoBotta/Gaussian-Mixture-VAE
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
https://github.com/EliPassov/classification-ensembles
https://github.com/EmGarr/kerod
https://github.com/Engineering-Course/LIP_JPPNet
Look into Person: Joint Body Parsing & Pose Estimation Network and A New Benchmark
https://github.com/Engineering-Course/LIP_SSL
Look into Person: Joint Body Parsing & Pose Estimation Network and A New Benchmark
https://github.com/Epsilon456/Face_LandMark_Recognition
https://github.com/ErikGDev/instance-segmentation
https://github.com/Fangyh09/pose_nms
https://github.com/FateScript/CenterNet-better
https://github.com/Feynman1999/MgeEditing
Video Super-Resolution with Recurrent Structure-Detail Network
https://github.com/Feynman1999/basicVSR_mge
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
https://github.com/Fickle519/stockforecastingmodelbasedon_XGboost
https://github.com/FirstPotatoMan/TensorflowJS_ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/Fivefold/SRCNN
https://github.com/Fuhui-Chen/TecoGAN
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/GAOwy123/py-faster-rcnn
https://github.com/GH3927/Mask-RCNN-applied-to-cranes
https://github.com/GH3927/Pix2Pix-applied-to-cranes
Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation
https://github.com/Gedeon-m-gedus/compressed_object_detection
https://github.com/George-Ogden/Mask-RCNN
https://github.com/GeyuanZhang/st-gcn-master
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/GilbertoQ/Bioinformatics
https://github.com/GitHubXlong/TecoGAN
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/GitTeaching/Predicting-using-Neural-ODE
https://github.com/GloryyrolG/MHEntropy
https://github.com/Golbstein/GlueStick-tensorflow/blob/main/gluestick.py
GlueStick: Robust Image Matching by Sticking Points and Lines Together
https://github.com/Gowti-AiboT/GAN-based-SISR-Keras
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.