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GART: Gaussian Articulated Template Models

Project Page; Arxiv; Youtube video; Bilibili video

<p float="left"> <img src="./assets/people_novel_pose_aist_demo.gif" width="128" height="128"> <img src="./assets/zju_novel_pose_aist_demo.gif" width="128" height="128"> <img src="./assets/hal9000_novel_pose_aist_demo.gif" width="128" height="128"> <img src="./assets/frog_novel_pose_aist_demo.gif" width="128" height="128"> <img src="./assets/spin.gif" width="128" height="128"> <img src="./assets/animation.gif" width="128" height="128"> </p>

teaser

<!-- ![main](./assets/MAIN.jpg) -->

Install

Our code is tested on Ubuntu 20.04 and cuda 11.8, you may have to change the following install script according to your system. To install, run:

bash install.sh

Prepare Data

Template Models and Poses

<details> <summary>SMPL for human body</summary>

Download SMPL v1.1 SMPL_python_v.1.1.0.zip from SMPL official website and move and rename SMPL_python_v.1.1.0/smpl/models/*.pkl to PROJECT_ROOT/data/smpl_model So you can get:

PROJECT_ROOT/data/smpl_model
    ├── SMPL_FEMALE.pkl
    ├── SMPL_MALE.pkl
    ├── SMPL_NEUTRAL.pkl

# to check the version of SMPL, here is the checksum of female pkl we are using
cksum SMPL_FEMALE.pkl
3668678829 247530000 SMPL_FEMALE.pkl
</details> <details> <summary>AMASS (generation only)</summary>

Download the SMPL-X N package of BMLrub subset from AMASS, unzip, and put it into PROJECT_ROOT/data/amass/BMLrub.

For generation application, you only have to download the SMPL models and AMASS poses. You can go to generation section directly and skip the following data downloading steps.

</details> <details> <summary>D-SMAL for dog body</summary>

If you want to work with dogs, please download smal_data from BITE and put this folder to lib_gart/smal/smal_data so you get:

lib_gart/smal/smal_data
    ├── mean_dog_bone_lengths.txt
    ├── my_smpl_data_SMBLD_v3.pkl
    ├── my_smpl_SMBLD_nbj_v3.pkl
    ├── new_dog_models
    │   ├── 39dogsnorm_newv3_dog_dog_0.obj
    ...
    │   └── X_scaled_39dogsnorm_newv3_dog.npy
    └── symmetry_inds.json
</details>

Real Videos

<details> <summary>ZJU-MoCap</summary>

We use the data from Instant-nvr. Note the poses from Instant-nvr is different from the original ZJU-MoCap, please follow the instructions in Instant-nvr to download their data: download ZJU-MoCap and their smpl model smpl-meta.tar.gz. link the unzipped data so you have:

PROJECT_ROOT/data/
    ├── smpl-meta
    │   ...
    │   └── SMPL_NEUTRAL.pkl
    └── zju_mocap
        ├── my_377
        ├── ...
        └── my_394
</details> <details> <summary>PeopleSnapshot</summary>

We use the data from InstantAvatar, including their pre-processed poses (already included when you clone this repo). First download the data from PeopleSnapshot. Then run DATA_ROOT=PATH_TO_UNZIPPED_PEOPLESNAPSHOT bash utils/preprocess_people_snapshot.sh to prepare the data.

</details> <details> <summary>Dog</summary>

Download our preprocessed data (pose estimated via BITE):

cd PROJECT_ROOT/data
gdown 1mPSnyLClyTwITPFrWDE70YOmJMXdnAZm
unzip dog_data_official.zip
</details> <details> <summary>UBC-Fashion</summary>

Download our preprocessed data (6 videos with pose estimated via ReFit):

cd PROJECT_ROOT/data
gdown 18byTvRqOqRyWOQ3V7lFOSV4EOHlKcdHJ
unzip ubc_release.zip
</details>

All the released data and logs can be downloaded from google drive.

Fit Real Videos

We provide examples for the four dataset in example.ipynb.

And all fitting and testing scripts can be found under script.

For example, if you want to fit all people snapshot video, run:

bash script/fit_people_30s.sh

You may found other useful scripts under script. The running time of each configuration is obtained from this laptop with an RTX-3080 GPU with 16GB vRAM and an Intel i9-11950H processor with 4x16GB RAM. We observe that for some unkown reason the training is slower on our A40 cluster.

Text2GART

Please see text2gart.ipynb for an example.

Acknowledgement

Our code is based on several interesting and helpful projects:

TODO