https://github.com/wu-cvgl/mvcontrol
Controllable Text-to-3D Generation via Surface-Aligned Gaussian Splatting
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/wustl-cig/FLAIR
FLAIR: A Conditional Diffusion Framework with Applications to Face Video Restoration
https://github.com/wuxiaolang/Cube_SLAM_wu
https://github.com/wuziyi616/multi_part_assembly
https://github.com/wwhio/megmodels
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/xaoch/rapJetson2
https://github.com/xar47x/pose
https://github.com/xcyan/eccv18_mtvae
MT-VAE: Learning Motion Transformations to Generate Multimodal Human Dynamics
https://github.com/xg416/DATUM
Recurrent Video Restoration Transformer with Guided Deformable Attention
https://github.com/xgd/waifu2xx
https://github.com/xggIoU/centernet_tensorflow_wilderface_voc
https://github.com/xiadanqing/Binary
https://github.com/xiaohaoChen/rrc_detection
Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection
https://github.com/xiaopengsu/CenterNet
https://github.com/xiaozeguo123/-Quick-Capture-system-HSR
https://github.com/ximilar-com/xcenternet
https://github.com/xingyizhou/pytorch-pose-hg-3d
Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
https://github.com/xingyuansun/pix3d
Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling
https://github.com/xinntao/BasicSR
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
https://github.com/xiuyu0000/new_papers_codes/tree/main/CLIFF
CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation
https://github.com/xiuyu0000/new_papers_codes/tree/main/DecoMR
https://github.com/xiuyu0000/new_papers_codes/tree/main/cmr
Convolutional Mesh Regression for Single-Image Human Shape Reconstruction
https://github.com/xiuyu0000/new_papers_codes/tree/main/rbpn
Recurrent Back-Projection Network for Video Super-Resolution
https://github.com/xiuyu0000/papers_with_examples/tree/main/Lite-HRNet
https://github.com/xiuyu0000/papers_with_examples/tree/main/maskrcnn
https://github.com/xiuyu0000/papers_with_examples/tree/main/mspn
Rethinking on Multi-Stage Networks for Human Pose Estimation
https://github.com/xjnpark/ds
https://github.com/xlearning-scu/2023-iccv-common
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/xmu-xiaoma666/External-Attention-pytorch
https://github.com/xmu-xiaoma666/External-Attention-pytorch/blob/master/attention/PolarizedSelfAttention.py
Polarized Self-Attention: Towards High-quality Pixel-wise Regression
https://github.com/xn1997/HigherHRnet
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
https://github.com/xoraus/Super-Resolution-CNN-for-Image-Restoration
https://github.com/xuannianz/keras-CenterNet
https://github.com/xuanqing94/torchdiffeq
https://github.com/xuhao1/VINS-Fisheye
A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors
https://github.com/xunhen/py-faster-rcnn-wjc
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.