202 lines
6.3 KiB
Markdown
202 lines
6.3 KiB
Markdown
# Gaussian Haircut：使用股线对齐 3D 高斯模型进行人体头发重建
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[**论文**](https://arxiv.org/abs/2409.14778) | [**项目页面**](https://eth-ait.github.io/GaussianHaircut/)
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本仓库包含了 Gaussian Haircut 的官方实现，这是一种基于股线的人体头发重建方法，用于单目视频。
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## 预览
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重建过程包括以下主要阶段：
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1. **预处理阶段**
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- 视频帧提取和整理
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- COLMAP相机重建
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- 头发和身体分割
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- 图像质量评估和筛选
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- 方向图计算
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- 人脸关键点检测
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- FLAME头部模型拟合
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2. **重建阶段**
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- 3D高斯体重建
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- FLAME网格拟合
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- 场景裁剪和优化
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- 头发股线重建
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3. **可视化阶段**
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- 导出重建的股线
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- Blender渲染可视化
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- 生成结果视频
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预期输出：
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```
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[your_scene_folder]/
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├── raw.mp4 # 输入视频
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├── 3d_gaussian_splatting/ # 3D高斯体重建结果
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├── flame_fitting/ # FLAME头部模型拟合结果
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├── strands_reconstruction/ # 头发股线重建中间结果
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├── curves_reconstruction/ # 最终头发股线结果
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└── visualization/ # 渲染结果和视频
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```
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## 入门指南
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### Linux 平台
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1. **安装 CUDA 11.8**
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按照 https://developer.nvidia.com/cuda-11-8-0-download-archive 上的说明进行操作。
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确保：
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- PATH 包含 <CUDA_DIR>/bin
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- LD_LIBRARY_PATH 包含 <CUDA_DIR>/lib64
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该环境仅在此 CUDA 版本下进行了测试。
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2. **安装 Blender 3.6** 以创建股线可视化
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按照 https://www.blender.org/download/lts/3-6 上的说明进行操作。
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3. **克隆仓库并运行安装脚本**
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```bash
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git clone git@github.com:eth-ait/GaussianHaircut.git
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cd GaussianHaircut
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chmod +x ./install.sh
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./install.sh
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```
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### Windows 平台
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1. **安装 CUDA 11.8**
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- 从 https://developer.nvidia.com/cuda-11-8-0-download-archive 下载并安装
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- 默认安装路径：C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8
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- 确保CUDA版本与系统兼容
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2. **安装 Blender 3.6**
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- 从 https://www.blender.org/download/lts/3-6 下载并安装
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- 默认安装路径：C:\Program Files\Blender Foundation\Blender 3.6
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3. **安装 Visual Studio 2019 Build Tools**
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- 从 https://visualstudio.microsoft.com/vs/older-downloads/ 下载并安装
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- 选择"C++构建工具"工作负载
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- 默认安装路径：C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools
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4. **安装 COLMAP**
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- 从 https://github.com/colmap/colmap/releases 下载并安装
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- 下载CUDA版本的COLMAP (例如：COLMAP-3.8-windows-cuda.zip)
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- 解压到不含空格的路径 (例如：C:\COLMAP)
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- 将COLMAP目录添加到系统PATH:
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1. 打开"系统属性" > "环境变量"
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2. 在"系统变量"中找到"Path"
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3. 点击"编辑" > "新建"
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4. 添加COLMAP目录路径
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5. 点击"确定"保存
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- 重启终端使PATH生效
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5. **安装 micromamba**
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- 从 https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html 下载
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6. **下载预训练模型和资源**
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```cmd
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# 在PowerShell中运行:
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.\download_resource.bat
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```
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这将下载并解压以下资源到resource目录:
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```
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resource/
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├── NeuralHaircut/ # Neural Haircut预训练模型
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├── Matte-Anything/ # SAM和GroundingDINO模型
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├── openpose/ # OpenPose模型
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└── hyperIQA/ # 图像质量评估模型
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```
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7. **克隆仓库并运行安装脚本**
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```cmd
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git clone https://github.com/Jeffreytsai1004/GaussianHairCut
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cd GaussianHairCut
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# 先下载所需要的资源
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.\download_resource.bat
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# 运行安装脚本
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.\install.bat
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# 运行重建脚本
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.\run.bat
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```
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## 重建
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1. **录制单目视频**
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- 参考项目页面上的示例视频
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- 录制要求：
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* 拍摄对象应缓慢转动头部，确保捕捉到所有角度
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* 保持头发和面部清晰可见
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* 避免快速移动导致的运动模糊
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* 保持光照条件稳定
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* 建议视频长度：10-20秒
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* 建议分辨率：1920x1080或更高
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2. **设置重建场景的目录**
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创建场景目录结构：
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```
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[your_scene_folder]/
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└── raw.mp4 # 您录制的视频文件
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```
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注意：
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- DATA_PATH 应指向包含 raw.mp4 的目录
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- 目录路径不应包含空格或特殊字符
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- 脚本会在此目录下自动创建所需的子目录
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- 确保有足够的磁盘空间(建议至少20GB)
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3. **运行脚本**
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Linux:
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```bash
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export PROJECT_DIR="[/path/to/]GaussianHaircut"
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export BLENDER_DIR="[/path/to/blender/folder/]blender"
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DATA_PATH="[path/to/scene/folder]" ./run.sh
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```
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Windows:
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```cmd
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# 在CMD中运行:
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set PROJECT_DIR=[path\to\]GaussianHaircut
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set DATA_PATH=[path\to\scene\folder]
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run.bat
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# 或在PowerShell中运行:
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set PROJECT_DIR=[path\to\]GaussianHaircut
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set DATA_PATH=[path\to\scene\folder]
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.\run.bat
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```
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该脚本执行数据预处理、重建以及使用 Blender 进行最终可视化。使用 Tensorboard 查看中间可视化结果。
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## 许可证
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此代码基于 3D Gaussian Splatting 项目。有关条款和条件，请参阅 LICENSE_3DGS。其余代码根据 CC BY-NC-SA 4.0 分发。
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如果此代码对您的项目有帮助，请引用以下论文。
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## 引用
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```
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@inproceedings{zakharov2024gh,
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title = {Human Hair Reconstruction with Strand-Aligned 3D Gaussians},
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author = {Zakharov, Egor and Sklyarova, Vanessa and Black, Michael J and Nam, Giljoo and Thies, Justus and Hilliges, Otmar},
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booktitle = {European Conference of Computer Vision (ECCV)},
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year = {2024}
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}
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```
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## 链接
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- [3D Gaussian Splatting](https://github.com/graphdeco-inria/gaussian-splatting)
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- [Neural Haircut](https://github.com/SamsungLabs/NeuralHaircut): FLAME 拟合管线、股线先验和发型扩散先验
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- [HAAR](https://github.com/Vanessik/HAAR): 头发上采样
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- [Matte-Anything](https://github.com/hustvl/Matte-Anything): 头发和身体分割
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- [PIXIE](https://github.com/yfeng95/PIXIE): FLAME 拟合的初始化
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- [Face-Alignment](https://github.com/1adrianb/face-alignment), [OpenPose](https://github.com/CMU-Perceptual-Computing-Lab/openpose): 用于 FLAME 拟合的关键点检测
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