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[AAAI 2025] Official Implementation of 3D$^2$-Actor: Learning Pose-Conditioned 3D-Aware Denoiser for Realistic Gaussian Avatar Modeling

Installation

  1. Clone this repo.
git clone https://github.com/silence-tang/GaussianActor.git
  1. Install environments.
conda create -n gaussianactor python=3.10
conda activate gaussianactor
pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2
(Install pytorch3d) Please download a proper version of pytorch3d from pytorch.org and install locally.
pip install pytorch_lightning==2.0.2
pip install lightning==2.0.2
pip install opencv-python scikit-image matplotlib einops packaging spconv pytorch-fid==0.3.0 lpips==0.1.4 tensorboard==2.12.0
(NOTE) If a module is missing, please install it manually based on the error message.
  1. Install 3DGS module
Clone AnimatableGaussians and place gaussians under GaussianActor/.
cd GaussianActor
cd gaussians/diff_gaussian_rasterization_depth_alpha
python setup.py install
cd ../..

ZJU-MoCap Dataset

  • Please download the preprocessed ZJU-MoCap dataset from the huggingface space, place CoreView_313.zip, CoreView_315.zip, CoreView_377.zip, CoreView_386.zip under GaussianActor/data/zju_mocap/ and finally unzip them.

SMPL/SMPL-X Data

  • Please download the preprocessed SMPL/SMPL-X data from the huggingface space, place body_models.zip under GaussianActor/ and finally unzip it.

Train the 2D denoiser

cd full
CUDA_VISIBLE_DEVICES=0 python train_2d_v1_ok_zju.py

Train the 3D rectifier

CUDA_VISIBLE_DEVICES=0 python train_v14_5_1_zju_lr5e-5.py

Test

(NOTE) You need to place the pretrained 2D/3D modeles (ckpt files) under GaussianActor/ckpt.
CUDA_VISIBLE_DEVICES=0 python test_v16_zju_np.py

Notes

  • You may try to enable DDP or mixed-precision training (mix-16) to accelerate the training process.