ML Research Hub

🌟 DG-Mesh: Constructing high-quality polygonal meshes from monocular video.

DG-Mesh reconstructs a high-quality dynamic 3D vertex-matched mesh from monocular video. The pipeline uses 3D Gaussian wavelets to represent dynamic scenes and differentiable algorithms to construct polygons.

DG-Mesh allows you to track the movement of vertices, simplifying the texturing of dynamic objects.
The method is memory efficient and fully differentiable, allowing optimization of the target object's 3D mesh directly.

The Github repository contains code for local training using datasets:

- D-NeRF
- DG-Mesh
- NeuralActor
- Custom dataset , shot on Iphone 14 Pro and processed in Record3D, RealityCheck and masked in DEVA.

🖥 Local launch:

conda create -n dg-mesh python=3.9
conda activate dg-mesh
conda install pytorch torchvision torcaudio pytorch-cuda=11.8 -c pytorch -c nvidia

#Install nvdiffrast
pip install git+https://github.com/NVlabs/tiny-cuda-nn#subdirectory=bindings/torch
pip install git+https://github.com/NVlabs/nvdiffrast/

# Install pytorch3d
export FORCE_CUDA=1
conda install -c fvcore -c iopath -c conda-forge fvcore iopath -y
pip install "git+https://github.com/facebookresearch/pytorch3d.git"

# Clone this repository
git clone https://github.com/Isabella98Liu/DG-Mesh.git
cd DG-Mesh

# Install submodules
pip install dgmesh/submodules/diff-gaussian-rasterization
pip install dgmesh/submodules/simple-knn

# Install other dependencies
pip install -r requirements.txt