This is the official repository for ECCV 2022 paper Skeleton-free Pose Transfer for Stylized 3D Characters.

[Paper] [Project page]

More detailed documentation coming soon!

• Python >= 3.7
• Pytorch >= 1.4
• Pytorch-geometric
• Pymesh (optional)

pip install opencv-python tensorboardx smplx pyrender open3d cython kornia

Download our demo data and pretrained model from here. Unzip it to the project root directory.

Then,

python demo.py

Checkt the results in ./demo/results and they should be the same as meshes in ./demo/results_reference.

To try with your own data, make sure the number of triangles is around 5K (not a strict requirement) and the orientation of the character is the same as demo data (front: +Z, up: +Y)

Download SMPLH model from here and extract. Set the SMPLH_PATH in global_var.py to the folder it is extracted to.

Then, run

python demo_smpl.py

More documentations about training will come soon.

All paths are defined in global_var.py

Download SMPLH model from here and extract. Set the SMPLH_PATH in global_var.py to the folder it is extracted to.

The training/testing split we used for the paper can be downloaded here.

1. Download Mixamo characters of T-pose.
2. Download Mixamo animations. Note that we don't use character-specific animations. Thus, simply download animations for one character.
3. Convert static characters (T-pose) into .obj format and animations into .bvh format. Unfortunately, I used some internal codes for this part, which cannot be open-sourced. You might have to write the conversion yourself.
4. Put characters of T-pose in {MIXAMO_PATH}/obj in .obj format.
5. Put ground truth skinning information of characters in {MIXAMO_PATH}/rig_info. The data format should be the same as in rig_info in RigNet dataset.
6. Run python data_proc/mixamo_simplify.py to make sure the triangle numbers are less than 5000.
7. Run python data_proc/mixamo_preproc_simplify.py
8. Run python data_proc/mixamo_motion_preproc.py

python train.py

Please cite our paper if you use this repository:

@inproceedings{liao2022pose,
    title = {Skeleton-free Pose Transfer for Stylized 3D Characters},
    author = {Liao, Zhouyingcheng and Yang, Jimei and Saito, Jun and Pons-Moll, Gerard and Zhou, Yang},
    booktitle = {European Conference on Computer Vision ({ECCV})},
    month = {October},
    organization = {{Springer}},
    year = {2022},
}

We borrowed part of the codes from the following projects:

https://github.com/zycliao/TailorNet_dataset
https://github.com/zhan-xu/RigNet
https://github.com/YadiraF/face3d
https://github.com/kzhou23/shape_pose_disent