daimakuai/gaussianavatars

Official repo for "GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians"

GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians

project / arxiv / video / bibtex

Hardware Requirements

• CUDA-ready GPU with Compute Capability 7.0+
• 12 GB VRAM (to train to paper evaluation quality)

Software Requirements

• Conda (recommended for easy setup)
• C++ Compiler for PyTorch extensions (we used Visual Studio 2019 for Windows, GCC for Linux)
• CUDA SDK 11 for PyTorch extensions, install after Visual Studio or GCC (we used 11.7, known issues with 11.6)
• C++ Compiler and CUDA SDK must be compatible
• FFMPEG to create result videos

Additional python packages

• RoMa (for rotation representations by default)
• PyTorch3D (for mesh loading and optionally rotation representations)
• DearPyGUI (for viewer interface)
• NVDiffRast (for mesh rendering in viewer)

Setup

Environment

Our default installation method is based on Conda package and environment management:

SET DISTUTILS_USE_SDK=1 # Windows only

git clone https://github.com/ShenhanQian/GaussianAvatars.git --recursive
cd GaussianAvatars

conda create --name gaussian-avatars -y python=3.10
conda activate gaussian-avatars

conda install ninja
conda install -c "nvidia/label/cuda-11.7.1" cuda-toolkit
ln -s "$CONDA_PREFIX/lib" "$CONDA_PREFIX/lib64"  # to avoid error "/usr/bin/ld: cannot find -lcudart"
pip install torch==2.0.1 torchvision==0.15.2  # match CUDA 11.7 by default

pip install -r requirements.txt  # can take a while for compiling pytorch3d and nvdiffrast

Data

Preprocessed NeRSemble dataset

We use 9 subjects from NeRSemble dataset in our paper. We provide the pre-processed data with this OneDrive link. To get access to the data, please

1. Request for the raw dataset here.
2. Request for the 9 pre-processed subjects here.

Please download the pre-processed data and decompress into data/.

FLAME

Our code and the pre-processed data relies on FLAME 2023. Downloaded assets from https://flame.is.tue.mpg.de/download.php and store them in below paths:

• assets/flame/flame2023.pkl # FLAME 2023 (versions w/ jaw rotation)
• assets/flame/FLAME_masks.pkl # FLAME Vertex Masks

It is possible to run our method with FLAME 2020 by download to assets/flame/generic_model.pkl. The FLAME_MODEL_PATH in flame_model/flame.py needs to be updated accordingly. And the FLAME tracking results should also be based on FLAME 2020 in this case.

Running

To run the optimizer, simply use

export SUBJECT=306
python train.py \
-s data/UNION10_${SUBJECT}_EMO1234EXP234589_v16_DS2-0.5x_lmkSTAR_teethV3_SMOOTH_offsetS_whiteBg_maskBelowLine \
-m output/UNION10EMOEXP_${SUBJECT}_eval_600k \
--port 60000 --eval --white_background --bind_to_mesh

Command Line Arguments for train.py

--source_path / -s

Path to the source directory containing a COLMAP or Synthetic NeRF data set.

--model_path / -m

Path where the trained model should be stored (output/<random> by default).

--eval

Add this flag to use a training/val/test split for evaluation.

--bind_to_mesh

Add this flag to bind 3D Gaussians to a driving mesh, e.g., FLAME.

--resolution / -r

Specifies resolution of the loaded images before training. If provided 1, 2, 4 or 8, uses original, 1/2, 1/4 or 1/8 resolution, respectively. For all other values, rescales the width to the given number while maintaining image aspect. If not set and input image width exceeds 1.6K pixels, inputs are automatically rescaled to this target.

--data_device

Specifies where to put the source image data, cuda by default, recommended to use cpu if training on large/high-resolution dataset, will reduce VRAM consumption, but slightly slow down training. Thanks to HrsPythonix.

--white_background / -w

Add this flag to use white background instead of black (default), e.g., for evaluation of NeRF Synthetic dataset.

--sh_degree

Order of spherical harmonics to be used (no larger than 3). 3 by default.

--convert_SHs_python

Flag to make pipeline compute forward and backward of SHs with PyTorch instead of ours.

--convert_cov3D_python

Flag to make pipeline compute forward and backward of the 3D covariance with PyTorch instead of ours.

--debug

Enables debug mode if you experience erros. If the rasterizer fails, a dump file is created that you may forward to us in an issue so we can take a look.

--debug_from

Debugging is slow. You may specify an iteration (starting from 0) after which the above debugging becomes active.

--iterations

Number of total iterations to train for, 30_000 by default.

--ip

IP to start GUI server on, 127.0.0.1 by default.

--port

Port to use for GUI server, 60000 by default.

--test_iterations

Space-separated iterations at which the training script computes L1 and PSNR over test set, 7000 30000 by default.

--save_iterations

Space-separated iterations at which the training script saves the Gaussian model, 7000 30000 <iterations> by default.

--checkpoint_iterations

Space-separated iterations at which to store a checkpoint for continuing later, saved in the model directory.

--start_checkpoint

Path to a saved checkpoint to continue training from.

--quiet

Flag to omit any text written to standard out pipe.

--feature_lr

Spherical harmonics features learning rate, 0.0025 by default.

--opacity_lr

Opacity learning rate, 0.05 by default.

--scaling_lr

Scaling learning rate, 0.005 by default.

--rotation_lr

Rotation learning rate, 0.001 by default.

--position_lr_max_steps

Number of steps (from 0) where position learning rate goes from initial to final. 30_000 by default.

--position_lr_init

Initial 3D position learning rate, 0.00016 by default.

--position_lr_final

Final 3D position learning rate, 0.0000016 by default.

--position_lr_delay_mult

Position learning rate multiplier (cf. Plenoxels), 0.01 by default.

--densify_from_iter

Iteration where densification starts, 500 by default.

--densify_until_iter

Iteration where densification stops, 15_000 by default.

--densify_grad_threshold

Limit that decides if points should be densified based on 2D position gradient, 0.0002 by default.

--densification_interal

How frequently to densify, 100 (every 100 iterations) by default.

--opacity_reset_interval

How frequently to reset opacity, 3_000 by default.

--lambda_dssim

Influence of SSIM on total loss from 0 to 1, 0.2 by default.

--percent_dense

Percentage of scene extent (0--1) a point must exceed to be forcibly densified, 0.01 by default.

By default, the trained models use all available images in the dataset. To train them while withholding a validation set and a test set for evaluation, use the --eval flag. Evaluation on the validation and test set will be conducted every --interval iterations. You can check the metrics in the terminal or within tensorboard.

Rendering

python render.py -m <path to trained model> # Generate renderings

Only render the validation set:

export SUBJECT=306
python render.py \
-m output/UNION10EMOEXP_${SUBJECT}_eval_600k \
--skip_train --skip_test

Only render the test set (and only render in the a front view):

export SUBJECT=306
python render.py \
-m output/UNION10EMOEXP_${SUBJECT}_eval_600k \
--skip_train --skip_val
--select_camera_id 8  # front view

Reenactment (and only render in the a front view):

export TGT_SUBJECT=218
export SUBJECT=306
python render.py \
-t data/UNION10_${TGT_SUBJECT}_EMO1234EXP234589_v16_DS2-0.5x_lmkSTAR_teethV3_SMOOTH_offsetS_whiteBg_maskBelowLine \
-m output/UNION10EMOEXP_${SUBJECT}_eval_600k \
--select_camera_id 8  # front view

Command Line Arguments for render.py

--model_path / -m

Path to the trained model directory you want to create renderings for.

--skip_train

Flag to skip rendering the training set.

--skip_val

Flag to skip rendering the test set.

--skip_test

Flag to skip rendering the validation set.

--quiet

Flag to omit any text written to standard out pipe.

--select_camera_id

Only render from a specific camera id.

--target_path / -t

Path to the target directory containing a motion sequence for reenactment.

The below parameters will be read automatically from the model path, based on what was used for training. However, you may override them by providing them explicitly on the command line.

--source_path / -s

Path to the source directory containing a COLMAP or Synthetic NeRF data set.

--images / -i

Alternative subdirectory for COLMAP images (images by default).

--eval

Add this flag to use a MipNeRF360-style training/test split for evaluation.

--resolution / -r

Changes the resolution of the loaded images before training. If provided 1, 2, 4 or 8, uses original, 1/2, 1/4 or 1/8 resolution, respectively. For all other values, rescales the width to the given number while maintaining image aspect. 1 by default.

--white_background / -w

Add this flag to use white background instead of black (default), e.g., for evaluation of NeRF Synthetic dataset.

--convert_SHs_python

Flag to make pipeline render with computed SHs from PyTorch instead of ours.

--convert_cov3D_python

Flag to make pipeline render with computed 3D covariance from PyTorch instead of ours.

Computing metrics

python metrics.py -m <path to trained model> # Compute error metrics on renderings

Command Line Arguments for metrics.py

--model_paths / -m

Space-separated list of model paths for which metrics should be computed.

Interactive Viewers

We provide two interactive viewers for our method: remote and real-time. Our viewing solutions are based on DearPyGUI.

Running the Remote Viewer

During training, one can monitor the training progress with the remote viewer

python remote_viewer.py --port 60000

Running the Local Viewer

After training, one can load and render the optimized 3D Gaussians with the local viewer

export SUBJECT=306
python local_viewer.py \
--point_path output/UNION10EMOEXP_${SUBJECT}_eval_600k

Acknowledgment

Our project is built on top of Gaussian Splatting. The GUI is inspired by INSTA. The mesh rendering operations are adapted from NVDiffRec and NVDiffRast. We sincerely thank the authors.

Cite

If you find our paper or code useful in your research, please cite with the following BibTeX entry:

@article{qian2023gaussianavatars,
  title={GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians},
  author={Qian, Shenhan and Kirschstein, Tobias and Schoneveld, Liam and Davoli, Davide and Giebenhain, Simon and Nie{\ss}ner, Matthias},
  journal={arXiv preprint arXiv:2312.02069},
  year={2023}
}