OptCtrlPoints: Finding the Optimal Control Points for Biharmonic 3D Shape Deformation, Pacific Graphics 2023

arXiv | Project Page
Kunho Kim*, Mikaela Angelina Uy*, Despoina Paschalidou, Alec Jacobson,Leonidas J. Guibas Minhyuk Sung

Introduction

This repository contains the official implementation of OptCtrlPoints: Finding the Optimal Control Points for Biharmonic 3D Shape Deformation.

OptCtrlPoints is a data-driven method to search for the optimal set of control points, enables accurate replication of the possible variations of a shape through biharmonic deformation.

We propose OptCtrlPoints, a data-driven framework designed to identify the optimal sparse set of control points for reproducing target shapes using biharmonic 3D shape deformation. Control-point-based 3D deformation methods are widely utilized for interactive shape editing, and their usability is enhanced when the control points are sparse yet strategically distributed across the shape. With this objective in mind, we introduce a data-driven approach that can determine the most suitable set of control points, assuming that we have a given set of possible shape variations. The challenges associated with this task primarily stem from the computationally demanding nature of the problem. Two main factors contribute to this complexity: solving a large linear system for the biharmonic weight computation and addressing the combinatorial problem of finding the optimal subset of mesh vertices. To overcome these challenges, we propose a reformulation of the biharmonic computation that reduces the matrix size, making it dependent on the number of control points rather than the number of vertices. Additionally, we present an efficient search algorithm that significantly reduces the time complexity while still delivering a nearly optimal solution. Experiments on SMPL, SMAL, and DeformingThings4D datasets demonstrate the efficacy of our method. Our control points achieve better template-to-target fit than FPS, random search, and neural-network-based prediction. We also highlight the significant reduction in computation time from days to approximately 3 minutes.

Get Started

Our code is tested with Python 3.9, CUDA 11.7 and Pytorch 1.13.1.

Installation

First, clone this repository:

git clone https://github.com/Soulmates2/OptCtrlPoints.git
cd OptCtrlPoints

Then you either can create a new conda environment:

conda env create -f env.yaml
conda activate optctrlpoints

or install essential packages into an existing environment:

pip install -r requirements.txt

Dataset preparation

Please see the detail in here

Run algorithm

After dataset preparation is done, you can run OptCtrlPoints by:

python experiments/run_optctrlpoints.py --config SMPL
python experiments/run_optctrlpoints.py --config SMAL

You can easily change the parameters by modifying config files.

Citation

If you find our work useful, please consider citing:

@article {kim2023optctrlpoints,
    journal = {Computer Graphics Forum},
    title = {{OptCtrlPoints: Finding the Optimal Control Points for Biharmonic 3D Shape Deformation}},
    author = {Kim, Kunho and Uy, Mikaela Angelina and Paschalidou, Despoina and Jacobson, Alec and Guibas, Leonidas J. and Sung, Minhyuk},
    year = {2023},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.14963}
}

Acknowledgement

We use SMPL, SMAL, and DeformingThings4D dataset. The data preprocessing code is borrowed from 3D-CODED.