MocapNET开源项目教程

MocapNET开源项目教程

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. 项目介绍

MocapNET是一个实时方法，用于从单目彩色图像中估计的2D身体关节直接估计3D人体姿态，并以流行的Bio Vision Hierarchy（BVH）格式输出。项目的主要贡献包括一个新颖且紧凑的2D姿态NSRM表示、一个人体姿态分类器以及一组面向人体姿态估计的方向调整神经网络。这些特性使得即使在显著遮挡的情况下，也能够恢复人体姿态。

2. 项目快速启动

首先，确保您的系统中已经安装了以下依赖项：

• Python
• Blender（用于3D渲染）
• Mediapipe（用于生成2D数据）

接下来，克隆项目仓库：

git clone https://github.com/FORTH-ModelBasedTracker/MocapNET.git
cd MocapNET

安装必要的Python包：

pip install -r requirements.txt

运行初始化脚本：

./initialize.sh

启动MocapNET：

python mocapnet.py

3. 应用案例和最佳实践

应用案例

• 实时人体姿态估计：将MocapNET集成到实时视频流中，进行人体姿态跟踪。
• 3D动画制作：使用MocapNET的输出BVH文件，结合Blender和MakeHuman插件制作3D动画。

最佳实践

• 性能优化：针对特定硬件进行性能优化，确保实时性能。
• 数据集准备：使用Mediapipe等工具准备高质量的2D数据集，以便进行训练和测试。

4. 典型生态项目

• BONSAPPS：一个AI人才开放项目，其中包括基于MocapNET的AUTO-MNET，用于3D人体姿态跟踪的汽车应用。
• Blender插件：MocapNET提供了Blender插件，可以与MakeHuman插件结合，用于生成自定义皮肤的3D人类动画。

以上就是MocapNET开源项目的教程，希望对您的学习和使用有所帮助。

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