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vroid_renderer

This repo converts and renders the 3D datasets introduced in PAniC-3D: Stylized Single-view 3D Reconstruction from Portraits of Anime Characters. As described in that repo, these scripts will add to ./_data/lustrous

setup

Make a copy of ./_env/machine_config.bashrc.template to ./_env/machine_config.bashrc, and set $PROJECT_DN to the absolute path of this repository folder. The other variables are optional.

This project requires docker with a GPU. Run these lines from the project directory to pull the image and enter a container; note these are bash scripts inside the ./make folder, not make commands. Alternatively, you can build the docker image yourself.

make/docker_pull
make/shell_docker
# OR
make/docker_build
make/shell_docker

vroid-dataset

The vroid-dataset should have downloaded folders of .vrm with their metadata to ./_data/lustrous/raw/vroid/[0-9]/*. This script renders those to ./_data/lustrous/renders/rutileE/

# run render script
python3 -m _scripts.render_all_vroid_rutileE

animerecon-benchmark

The animerecon-benchmark should have downloaded compressed files to ./_data/lustrous/raw/[genshin,hololive]. Decompress all these files to a temp directory; each file becomes a directory carrying a .pmx MMD model. Using a DSSc converter, go to PMX-to-VRM > Batch, and select ./_data/lustrous/raw/dssc/dssc_mapping_daredemoE.txt. This should convert and put files to ./_data/lustrous/raw/dssc/[genshin,hololive]/*.vrm. The following script renders the .vrm files to ./_data/lustrous/renders/daredemoE/

# run render script
python3 -m _scripts.render_all_animerecon_daredemoE

(Thanks to Softmind Ltd. for sharing their DanSingSing converter, and Geng Lin for adding the batch function)

citing

If you use our repo, please cite our work:

@inproceedings{chen2023panic3d,
    title={PAniC-3D: Stylized Single-view 3D Reconstruction from Portraits of Anime Characters},
    author={Chen, Shuhong and Zhang, Kevin and Shi, Yichun and Wang, Heng and Zhu, Yiheng and Song, Guoxian and An, Sizhe and Kristjansson, Janus and Yang, Xiao and Matthias Zwicker},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2023}
}