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Fast-SNARF: A Fast Deformer for Articulated Neural Fields

Paper

Fast-SNARF models the articulation of neural fields. It allows learning articulated objects from deformed observations, such as 3D posed meshes, in an accurate and fast way. In particular, Fast-SNARF achieves similar accuracy as its parent SNARF while being 150x faster.

In this repo, we apply Fast-SNARF to learn animatable avatars from 3D data. More application (e.g. learning from images) will be announced later.

Quick Start

Clone this repo:

git clone https://github.com/xuchen-ethz/fast-snarf.git
cd fast-snarf

Install environment:

conda env create -f environment.yml
conda activate fast_snarf
python setup.py install

Download SMPL models (1.0.0 for Python 2.7 (10 shape PCs)) and move them to the corresponding places:

mkdir lib/smpl/smpl_model/
mv /path/to/smpl/models/basicModel_f_lbs_10_207_0_v1.0.0.pkl lib/smpl/smpl_model/SMPL_FEMALE.pkl
mv /path/to/smpl/models/basicmodel_m_lbs_10_207_0_v1.0.0.pkl lib/smpl/smpl_model/SMPL_MALE.pkl

Download our pretrained models and test motion sequences:

sh ./download_data.sh

Run a quick demo for clothed human:

python demo.py expname=cape subject=3375 demo.motion_path=data/aist_demo/seqs +experiments=cape

You can the find the video in outputs/cape/3375/demo.mp4 and images in outputs/cape/3375/images/. To save the meshes, add demo.save_mesh=true to the command.

You can also try other subjects (see outputs/data/cape for available options) by setting subject=xx, and other motion sequences from AMASS by setting demo.motion_path=/path/to/amass_modetion.npz.

Some motion sequences have high fps and one might want to skip some frames. To do this, add demo.every_n_frames=x to consider every x frame in the motion sequence. (e.g. demo.every_n_frames=10 for PosePrior sequences)

Training and Evaluation

Minimally Clothed Human

Prepare Datasets

Download the AMASS dataset. We use ''DFaust Snythetic'' and ''PosePrior'' subsets and SMPL-H format. Unzip the dataset into data folder.

tar -xf DFaust67.tar.bz2 -C data
tar -xf MPILimits.tar.bz2 -C data

Preprocess dataset:

python preprocess/sample_points.py --output_folder data/DFaust_processed
python preprocess/sample_points.py --output_folder data/MPI_processed --skip 10 --poseprior

Training

Run the following command to train for a specified subject:

python train.py subject=50002

Training logs are available on wandb (registration needed, free of charge). It should take ~25 minutes on a single 2080Ti.

Evaluation

Run the following command to evaluate the method for a specified subject on within distribution data (DFaust test split):

python test.py subject=50002

and outside destribution (PosePrior):

python test.py subject=50002 datamodule=jointlim

Generate Animation

You can use the trained model to generate animation (same as in Quick Start):

python demo.py expname='dfaust' subject=50002 demo.motion_path='data/aist_demo/seqs'

Clothed Human

Training

Download the CAPE dataset and unzip into data folder.

Run the following command to train for a specified subject and clothing type:

python train.py datamodule=cape subject=3375 datamodule.clothing='blazerlong' +experiments=cape

Training logs are available on wandb. It should take 1 hour on a single 2080Ti.

Generate Animation

You can use the trained model to generate animation (same as in Quick Start):

python demo.py expname=cape subject=3375 demo.motion_path=data/aist_demo/seqs +experiments=cape

Reference

If you find our code or paper useful, please cite as

@article{Chen2023PAMI,
  author = {Xu Chen and Tianjian Jiang and Jie Song and Max Rietmann and Andreas Geiger and Michael J. Black and Otmar Hilliges},
  title = {Fast-SNARF: A Fast Deformer for Articulated Neural Fields},
  journal = {Pattern Analysis and Machine Intelligence (PAMI)},
  year = {2023}
}

@inproceedings{chen2021snarf,
  title={SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapes},
  author={Chen, Xu and Zheng, Yufeng and Black, Michael J and Hilliges, Otmar and Geiger, Andreas},
  booktitle={International Conference on Computer Vision (ICCV)},
  year={2021}
}

Acknowledgement

We use the pre-processing code in PTF and LEAP with some adaptions (./preprocess). The network and sampling part of the code (lib/model/network.py and lib/model/sample.py) is implemented based on IGR and IDR. The code for extracting mesh (lib/utils/meshing.py) is adapted from NASA. Our implementation of Broyden's method (lib/model/broyden.py) is based on DEQ. We sincerely thank these authors for their awesome work.