登录
首页 » Python » DensePose-master

DensePose-master

于 2019-06-17 发布 文件大小:9846KB
0 237
下载积分: 1 下载次数: 2

代码说明:

  DensePose用深度学习把2D图像坐标映射到3D人体表面上,再加上以每秒多帧的速度处理密集坐标,最后实现动态人物的精确定位和姿态估计。该技术集目标检测、姿态估计、目标部分/实例分割等多种计算机视觉任务于一身的一个综合问题。(DensePost maps 2D image coordinates to 3D human body surface by in-depth learning, and processes dense coordinates at the speed of multiple frames per second. Finally, it realizes precise positioning and attitude estimation of dynamic characters. This technology integrates many kinds of computer vision tasks, such as target detection, attitude estimation, target part/instance segmentation, etc.)

文件列表:

DensePose-master, 0 , 2018-08-28
DensePose-master\.gitignore, 495 , 2018-08-28
DensePose-master\CMakeLists.txt, 2012 , 2018-08-28
DensePose-master\CODE_OF_CONDUCT.md, 286 , 2018-08-28
DensePose-master\CONTRIBUTING.md, 1250 , 2018-08-28
DensePose-master\DensePoseData, 0 , 2018-08-28
DensePose-master\DensePoseData\demo_data, 0 , 2018-08-28
DensePose-master\DensePoseData\demo_data\demo_dp_single_ann.pkl, 980845 , 2018-08-28
DensePose-master\DensePoseData\demo_data\demo_im.jpg, 149246 , 2018-08-28
DensePose-master\DensePoseData\demo_data\synth_UV_example.png, 25997 , 2018-08-28
DensePose-master\DensePoseData\demo_data\texture_atlas_200.png, 1237384 , 2018-08-28
DensePose-master\DensePoseData\demo_data\texture_from_SURREAL.png, 831242 , 2018-08-28
DensePose-master\DensePoseData\get_DensePose_COCO.sh, 346 , 2018-08-28
DensePose-master\DensePoseData\get_densepose_uv.sh, 163 , 2018-08-28
DensePose-master\DensePoseData\get_eval_data.sh, 173 , 2018-08-28
DensePose-master\DensePoseData\infer_out, 0 , 2018-08-28
DensePose-master\DensePoseData\infer_out\demo_im.jpg.pdf, 1056420 , 2018-08-28
DensePose-master\DensePoseData\infer_out\demo_im_INDS.png, 8801 , 2018-08-28
DensePose-master\DensePoseData\infer_out\demo_im_IUV.png, 77229 , 2018-08-28
DensePose-master\GETTING_STARTED.md, 2815 , 2018-08-28
DensePose-master\INSTALL.md, 6712 , 2018-08-28
DensePose-master\LICENSE, 19333 , 2018-08-28
DensePose-master\MODEL_ZOO.md, 3109 , 2018-08-28
DensePose-master\Makefile, 491 , 2018-08-28
DensePose-master\NOTICE, 1344 , 2018-08-28
DensePose-master\PoseTrack, 0 , 2018-08-28
DensePose-master\PoseTrack\DensePose-PoseTrack-Visualize.ipynb, 792870 , 2018-08-28
DensePose-master\PoseTrack\README.md, 3352 , 2018-08-28
DensePose-master\PoseTrack\configs, 0 , 2018-08-28
DensePose-master\PoseTrack\configs\DensePose_ResNet50_FPN_s1x-e2e.yaml, 1976 , 2018-08-28
DensePose-master\PoseTrack\get_DensePose_PoseTrack.sh, 696 , 2018-08-28
DensePose-master\README.md, 3216 , 2018-08-28
DensePose-master\challenge, 0 , 2018-08-28
DensePose-master\challenge\2018_COCO_DensePose, 0 , 2018-08-28
DensePose-master\challenge\2018_COCO_DensePose\data_format.md, 3303 , 2018-08-28
DensePose-master\challenge\2018_COCO_DensePose\evaluation.md, 4694 , 2018-08-28
DensePose-master\challenge\2018_COCO_DensePose\example_results.json, 181922 , 2018-08-28
DensePose-master\challenge\2018_COCO_DensePose\readme.md, 3797 , 2018-08-28
DensePose-master\challenge\2018_COCO_DensePose\results_format.md, 2153 , 2018-08-28
DensePose-master\challenge\2018_COCO_DensePose\upload.md, 3823 , 2018-08-28
DensePose-master\challenge\2018_PoseTrack_DensePose, 0 , 2018-08-28
DensePose-master\challenge\2018_PoseTrack_DensePose\data_format.md, 4449 , 2018-08-28
DensePose-master\challenge\2018_PoseTrack_DensePose\evaluation.md, 4641 , 2018-08-28
DensePose-master\challenge\2018_PoseTrack_DensePose\readme.md, 3594 , 2018-08-28
DensePose-master\challenge\2018_PoseTrack_DensePose\results_format.md, 2157 , 2018-08-28
DensePose-master\challenge\2018_PoseTrack_DensePose\upload.md, 3873 , 2018-08-28
DensePose-master\challenge\encode_results_for_competition.py, 3508 , 2018-08-28
DensePose-master\cmake, 0 , 2018-08-28
DensePose-master\cmake\Summary.cmake, 1020 , 2018-08-28
DensePose-master\cmake\legacy, 0 , 2018-08-28
DensePose-master\cmake\legacy\Cuda.cmake, 9531 , 2018-08-28
DensePose-master\cmake\legacy\Dependencies.cmake, 1341 , 2018-08-28
DensePose-master\cmake\legacy\Modules, 0 , 2018-08-28
DensePose-master\cmake\legacy\Modules\FindCuDNN.cmake, 2100 , 2018-08-28
DensePose-master\cmake\legacy\Summary.cmake, 940 , 2018-08-28
DensePose-master\cmake\legacy\Utils.cmake, 10724 , 2018-08-28
DensePose-master\cmake\legacy\legacymake.cmake, 1621 , 2018-08-28
DensePose-master\configs, 0 , 2018-08-28
DensePose-master\configs\DensePoseKeyPointsMask_ResNet50_FPN_s1x-e2e.yaml, 2681 , 2018-08-28
DensePose-master\configs\DensePose_ResNet101_FPN.yaml, 1975 , 2018-08-28
DensePose-master\configs\DensePose_ResNet101_FPN_32x8d_s1x-e2e.yaml, 2151 , 2018-08-28
DensePose-master\configs\DensePose_ResNet101_FPN_32x8d_s1x.yaml, 2155 , 2018-08-28
DensePose-master\configs\DensePose_ResNet101_FPN_s1x-e2e.yaml, 1977 , 2018-08-28
DensePose-master\configs\DensePose_ResNet101_FPN_s1x.yaml, 2117 , 2018-08-28
DensePose-master\configs\DensePose_ResNet50_FPN.yaml, 1974 , 2018-08-28
DensePose-master\configs\DensePose_ResNet50_FPN_s1x-e2e.yaml, 1975 , 2018-08-28
DensePose-master\configs\DensePose_ResNet50_FPN_s1x.yaml, 2115 , 2018-08-28
DensePose-master\configs\DensePose_ResNet50_FPN_single_GPU.yaml, 2109 , 2018-08-28
DensePose-master\configs\rpn_densepose_only_R-50-FPN_1x.yaml, 1075 , 2018-08-28
DensePose-master\configs\rpn_densepose_only_X-101-32x8d-FPN_1x.yaml, 1263 , 2018-08-28
DensePose-master\detectron, 0 , 2018-08-28
DensePose-master\detectron\__init__.py, 0 , 2018-08-28
DensePose-master\detectron\core, 0 , 2018-08-28
DensePose-master\detectron\core\__init__.py, 0 , 2018-08-28
DensePose-master\detectron\core\config.py, 47570 , 2018-08-28
DensePose-master\detectron\core\rpn_generator.py, 9280 , 2018-08-28
DensePose-master\detectron\core\test.py, 37386 , 2018-08-28
DensePose-master\detectron\core\test_engine.py, 15303 , 2018-08-28
DensePose-master\detectron\core\test_retinanet.py, 7104 , 2018-08-28
DensePose-master\detectron\datasets, 0 , 2018-08-28
DensePose-master\detectron\datasets\VOCdevkit-matlab-wrapper, 0 , 2018-08-28
DensePose-master\detectron\datasets\VOCdevkit-matlab-wrapper\get_voc_opts.m, 231 , 2018-08-28
DensePose-master\detectron\datasets\VOCdevkit-matlab-wrapper\voc_eval.m, 1332 , 2018-08-28
DensePose-master\detectron\datasets\VOCdevkit-matlab-wrapper\xVOCap.m, 258 , 2018-08-28
DensePose-master\detectron\datasets\__init__.py, 0 , 2018-08-28
DensePose-master\detectron\datasets\cityscapes_json_dataset_evaluator.py, 2960 , 2018-08-28
DensePose-master\detectron\datasets\coco_to_cityscapes_id.py, 2495 , 2018-08-28
DensePose-master\detectron\datasets\data, 0 , 2018-08-28
DensePose-master\detectron\datasets\data\README.md, 3187 , 2018-08-28
DensePose-master\detectron\datasets\dataset_catalog.py, 8164 , 2018-08-28
DensePose-master\detectron\datasets\densepose_cocoeval.py, 39088 , 2018-08-28
DensePose-master\detectron\datasets\dummy_datasets.py, 1899 , 2018-08-28
DensePose-master\detectron\datasets\json_dataset.py, 20722 , 2018-08-28
DensePose-master\detectron\datasets\json_dataset_evaluator.py, 19172 , 2018-08-28
DensePose-master\detectron\datasets\roidb.py, 7497 , 2018-08-28
DensePose-master\detectron\datasets\task_evaluation.py, 14367 , 2018-08-28
DensePose-master\detectron\datasets\voc_dataset_evaluator.py, 6719 , 2018-08-28
DensePose-master\detectron\datasets\voc_eval.py, 7386 , 2018-08-28
DensePose-master\detectron\modeling, 0 , 2018-08-28
DensePose-master\detectron\modeling\FPN.py, 20218 , 2018-08-28

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • mesh
    dish tracking techniques
    2014-08-19 15:43:37下载
    积分:1
  • 均匀方格法点云精简
    能实现最小包围盒法方格划分,并利用中值进行点云精简(The minimum bounding box method can be implemented and the median value can be used to simplify the point cloud.)
    2020-11-23 16:09:34下载
    积分:1
  • Clustering-matlab-master
    说明:  k-means,DB-SCAN,基于密度峰值的聚类算法的matlab简单实践Clustering-matlab-master(k-means,DB-SCAN,Clustering-matlab-master of Simple practice of matlab based on density Peak clustering)
    2019-12-25 16:20:42下载
    积分:1
  • GuideFilter 像滤波
    Guilde Filter 图像滤波; 包含[2015]Fast Guilded filter.pdf ; [2013] Guided Image Filtering.pdf 经典。(inculde classical paper for guilded filter)
    2020-06-18 03:00:02下载
    积分:1
  • my_hausdroff2
    说明:  利用图象边缘特征的基于豪思道夫距离的景象匹配算法,匹配速度要比基于灰度的算法高好几个数量级(use of the images on the edge of Ho Sze-Astoria distance from the scene matching algorithm, speed matching gray than the algorithm based on several high-volume)
    2006-05-16 17:26:23下载
    积分:1
  • 机载正侧视RD
    说明:  机载正侧视SAR雷达仿真和成像,使用matlab编写,直接运行可用(Simulation and imaging of airborne forward-looking SAR radar)
    2020-06-17 13:00:02下载
    积分:1
  • ZxingScanner2.3
    运用core2.1的架构包来实现打开相机扫描二维、条形码的例子有详细的注释(Core2.1 use open architecture package to achieve the two-dimensional scanning camera, barcode examples detailed notes)
    2015-09-06 10:24:14下载
    积分:1
  • pianzhen
    可实现偏振图像合成以得到强度图像,偏振度图像等(Polarization image synthesis can be achieved to obtain intensity images, polarization images, etc.)
    2018-04-27 10:29:26下载
    积分:1
  • 6feab176-4b44-4e7e-a6ea-987e84fa1fd0
    本书是第一本也是唯一一本详细介绍LOD(Level of Detail)的书。本书介绍了LOD的理论和多种实现的算法,讨论了LOD的实际应用,包括游戏编程中的优化、地形的优化等。本书是所有从事计算机图形领域的专业人士所必须参考的书,包括计算机游戏编程、医学可视化、计算机辅助设计,以及虚拟现实系统等等。(This book is the first and only one detail LOD (Level of Detail) book. This book introduces the theory and a variety of LOD realization algorithm, discussed the practical application of LOD, including game programming in the optimization, optimization, such as terrain. This book is all persons engaged in the field of computer graphics professionals who need to reference books, including computer game programming, medical visualization, computer-aided design and virtual reality systems.)
    2008-06-11 21:36:38下载
    积分:1
  • tuxingxue
    这是一本关于图形学实验的指导书里面有对图形学的具体讲解(tuxingxue)
    2009-05-18 00:21:46下载
    积分:1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载