We propose a zero-shot text-driven 3D shape deformation system that deforms an input 3D mesh of a manufactured object to fit an input text description. To do this, our system optimizes the parameters of a deformation model to maximize an objective function based on the widely used pre-trained vision language model CLIP. We find that CLIP-based objective functions exhibit many spurious local optima; to circumvent them, we parameterize deformations using a novel deformation model called BoxDefGraph which our system automatically computes from an input mesh, the BoxDefGraph is designed to capture the object aligned rectangular/circular geometry features of most manufactured objects. We then use the CMA-ES global optimization algorithm to maximize our objective, which we find to work better than popular gradient-based optimizers. We demonstrate that our approach produces appealing results and outperforms several baselines.
Cite
@article{arxiv.2410.15199,
title = {CLIPtortionist: Zero-shot Text-driven Deformation for Manufactured 3D Shapes},
author = {Xianghao Xu and Srinath Sridhar and Daniel Ritchie},
journal= {arXiv preprint arXiv:2410.15199},
year = {2024}
}