Fur reconstruction from multi-view images using 3D Gaussian Splatting.

• CUDA 11.8 (download)
• Conda
• Blender 3.6 (optional, for strand visualization) (download)

Make sure PATH includes <CUDA_DIR>/bin and LD_LIBRARY_PATH includes <CUDA_DIR>/lib64.

cd submodules/GaussianHaircut bash install.sh

This will clone external dependencies (pytorch3d, simple-knn, glm), create a conda environment, and install all required packages. NeuralHaircut is already included in ext/.

Download the preprocessed data from Google Drive (link) and place it into your desired location, e.g.:

/path/to/data/panda_processed_GH2/walk/ 

pip install gdown gdown https://drive.google.com/uc?id=1rIxvQKXVaMZ6Xzx7MAmHfucnbezLSbZt unzip *.zip 

The data directory should contain:

• sdf_grid.npy, min_bound.npy, max_bound.npy -- SDF volume for penetration loss
• neus_lr.obj -- reconstructed mesh (used for chamfer loss)
• furless.obj -- furless body mesh (used for strand initialization)
• tan_furless.npy -- precomputed tangent directions
• annotations_furless_reshaped2.json -- body part annotations (controls per-region fur length and gravity)
• eyes.ply -- eye landmarks (used for metric scale estimation)
• images/ -- input images
• masks/ -- segmentation masks
• orientations/ -- orientation maps

Edit submodules/GaussianHaircut/simple_run_panda.sh and set your paths:

CUDA_HOME=/path/to/cuda-11.8 # your CUDA installation DATA_PATH=/path/to/data/panda_processed_GH2/walk/ ENV_PATH=/path/to/conda/env # conda environment name or path

DATA_PATH is passed to the training and export scripts via --data_root, which automatically replaces the DATA_ROOT placeholder in the YAML config. No need to edit the YAML config separately.

The YAML config (src/arguments/metrical_panda_furless_15k_small.yaml) contains animal-specific parameters like per-region fur length, gravity directions, and loss settings. These don't need to change between runs of the same animal.

cd submodules/GaussianHaircut bash simple_run_panda.sh

The pipeline runs two stages:

1. Fur strand reconstruction -- optimizes strand geometry and appearance using orientation, mask, chamfer, SDF, shape consistency, and gravity losses
2. Strand export -- exports the reconstructed strands as a .ply point cloud

Results are saved to the SAVE_EXP_PATH directory. Use Tensorboard to monitor training progress.

NeuralFur/ submodules/ GaussianHaircut/ src/ train_latent_fur.py # main training script preprocessing/export_fur.py # strand export scene/ # scene, cameras, gaussian models gaussian_renderer/ # rendering utils/ # losses, camera utils, etc. arguments/ # config and CLI argument parsing ext/ NeuralHaircut/ # strand prior, texture networks diff_gaussian_rasterization_hair/ # custom CUDA rasterizer simple_run_panda.sh # example run script install.sh # installation script 

• Code for processed scenes
• Reconstruction results
• Preprocessing pipeline for new data (deadline: April 12, 2026)

If you find this work useful, please consider citing:

@article{sklyarova_kabadayi_2025neuralfur, title = {NeuralFur: Animal Fur Reconstruction from Multi-view Images}, author = {Sklyarova, Vanessa and Kabadayi, Berna and Yiannakidis, Anastasios and Becherini, Giorgio and Black, Michael J. and Thies, Justus}, journal = {arXiv}, month = {January}, year = {2026} }