MocapNET开源项目使用教程

MocapNET开源项目使用教程

MocapNET We present MocapNET, a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose NSRM representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the Human 3.6 Million (H3.6M) dataset compared to the baseline method (MocapNET) while maintaining real-time performance 项目地址: https://gitcode.com/gh_mirrors/mo/MocapNET

1. 项目目录结构及介绍

MocapNET项目的目录结构如下：

• cmake：存放CMake构建系统相关的文件。
• dataset：包含项目所使用的数据集。
• dependencies：存放项目依赖的库和模块。
• doc：项目的文档资料。
• docker：包含Docker相关的配置和脚本。
• scripts：存放项目运行所需的脚本文件。
• src：项目的源代码目录。
• CMakeLists.txt：CMake项目的配置文件。
• README.md：项目的说明文档。
• citation.bib：项目的引用文献。
• initialize.sh、revert.sh、update.sh：项目的初始化、回滚和更新脚本。

每个目录和文件都承担着项目的不同部分，确保项目能够顺利运行和开发。

2. 项目的启动文件介绍

项目的启动主要通过以下文件进行：

• initialize.sh：项目的初始化脚本，用于设置项目环境，安装依赖等。
• src目录中的.py文件：这些是项目的源代码文件，可以直接运行以启动项目的不同功能。

启动项目之前，需要确保所有的依赖都已正确安装，并且环境变量设置正确。

3. 项目的配置文件介绍

项目的配置主要通过以下文件进行：

• CMakeLists.txt：这是CMake的项目配置文件，用于定义编译过程和项目结构。
• 在src目录中的配置文件：可能包括.json、.yaml或.ini等格式的配置文件，这些文件用于配置项目的各种参数，如数据集路径、模型参数等。

在开始使用项目之前，需要检查并根据实际情况修改这些配置文件，确保项目能够使用正确的设置进行编译和运行。

通过上述介绍，用户可以更好地理解MocapNET项目的结构，并能够根据需要进行配置和使用。

MocapNET We present MocapNET, a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose NSRM representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the Human 3.6 Million (H3.6M) dataset compared to the baseline method (MocapNET) while maintaining real-time performance 项目地址: https://gitcode.com/gh_mirrors/mo/MocapNET