license: mit
task_categories:
- graphics
tags:
- 3d
- face
- animation
- retargeting
- siggraph
size_categories:
- 1K<n<10K
NFR sample training data
Sample data pack for NFR (Neural Face Rigging),
SIGGRAPH 2023. It covers the ICT_single_coarse dataset (40 identities) and is
meant to get the training pipeline running without downloading the full ~800GB
set used in the paper.
What is in here
Only the files that cannot be regenerated are shipped (3.0GB):
data/ICT_single_coarse/{train,val,test}/*_processed_matrix.pkl # 40 x 75MB, deformation gradients
data/ICT_single_coarse/*_neutral.obj # 40 neutral meshes
data/ICT_single_coarse/iden_exp_weights_single.npz # identity / expression coefficients
data/ICT_single_coarse/ICT_single_exp_weights.npy # per-expression weights
data/ICT_single_coarse/gradients_std.npy
data/ICT_live_100/gradients_std.npy # referenced by std_file
data/MF_all_v5/img_stat.pkl # referenced by img_file
Each _processed_matrix.pkl holds 266 expressions of one identity: verts
(266, 3694, 3) and gradients (266, 7007, 9).
Usage
Download and unpack at the repository root:
from huggingface_hub import snapshot_download
snapshot_download(repo_id="HKU-CGVU/NFR-sample-data", repo_type="dataset",
local_dir=".", allow_patterns="data/*")
allow_patterns="data/*" keeps this dataset card from landing on top of the
repository's own readme.md on case-insensitive filesystems (macOS, Windows).
Then regenerate the derived tensors (renders, DiffusionNet gradients and spectral decompositions):
python dataset_preprocess.py \
--data_dir ./data/ICT_single_coarse \
--save_data_dir ./data/ICT_single_coarse \
-pi -pg -pd --num_threads 8
This adds about 32GB and takes roughly 30 minutes. Then train:
python FWH_run.py -c config/train_single.yaml
Training this configuration needs about 24GB of RAM and a single GPU. See the Training section of the repository readme for details.
Source
Derived from ICT-FaceKit. Please also follow the ICT-FaceKit license terms.
Citation
@inproceedings{qin2023NFR,
title={Neural Face Rigging for Animating and Retargeting Facial Meshes in the Wild},
author={Qin, Dafei and Saito, Jun and Aigerman, Noam and Groueix, Thibault and Komura, Taku},
booktitle={SIGGRAPH 2023 Conference Papers},
year={2023}
}