iarap 0.1.0

ARAP regularization for implicit surfaces

ARAP-Regularization-for-Implicit-Surfaces

This is the source code for the paper As-Rigid-As-Possible Regularization for Implicit Surfaces, SGP 2026.

Installation

The only dependency is pytorch (tested with torch 2.11.0 with CUDA 12.8). For the demos that generate results similar to those in the paper (implict surface modeling and Gauss Stylization) further dependencies are required (see demos/requirements.txt).

python -m venv .env
.\.venv\Scripts\Activate.ps1 # depends on your os
pip install git+https://gitlab.com/tobidju/arap-regularization-for-implicit-surfaces.git

Usage (iarap.arap_energy)

iarap.arap_energy computes the batch-averaged ARAP regularization energy ($\mathcal{L}_{ARAP}$) for a deformation model $\boldsymbol f$.

• Input:

  f: deformation function as torch.nn.Module $(B, d) \rightarrow (B, d)$

  samples: points of shape $(B, d)$ (tested with $d=2$ and $d=3$)

  l_bend: bending weight ($\lambda_{bend}$)

• Output: scalar tensor (mean ARAP energy)

Minimal example:

import torch
import iarap

class Identity(torch.nn.Module):
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return x

f = Identity()
samples = torch.randn(1024, 3, device="cuda" if torch.cuda.is_available() else "cpu")
loss = iarap.arap_energy(f, samples, l_bend=0.1)
print(loss.item())

Demos

Please see ./demos/surface_modeling.ipynb to see how ARAP regularization can be used to model implicit surfaces with user defined handles. In ./demos/gauss_stylization.ipynb you will find the implict variant of Gauss Stylization as described in the paper.

There are multiple pretrained signed distance functions in ./demos/sdfs. Please note that some sdfs are based on meshes that are under CC-BY.

Citation

If you use the repository, please cite

@article{10.1111:cgf.70519,
  journal = {Computer Graphics Forum},
  title = {{As-Rigid-As-Possible Regularization for Implicit Surfaces}},
  author = {Djuren, Tobias and 
    Worchel, Markus and 
    Finnendahl, Ugo and 
    Alexa, Marc},
  year = {2026},
  publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
  ISSN = {1467-8659},
  DOI = {10.1111/cgf.70519}
}