One-to-many Reconstruction of 3D Geometry of cultural Artifacts using a synthetically trained Generative Model

Thomas Pöllabauer, Julius Kühn, Jiayi Li, Arjan Kuijper
2/13/2024
cs.CVcs.AI

No confident code implementation found for this paper.

Abstract

Estimating the 3D shape of an object using a single image is a difficult problem. Modern approaches achieve good results for general objects, based on real photographs, but worse results on less expressive representations such as historic sketches. Our automated approach generates a variety of detailed 3D representation from a single sketch, depicting a medieval statue, and can be guided by multi-modal inputs, such as text prompts. It relies solely on synthetic data for training, making it adoptable even in cases of only small numbers of training examples. Our solution allows domain experts such as a curators to interactively reconstruct potential appearances of lost artifacts.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{pllabauer2024onetomany,
  title  = {One-to-many Reconstruction of 3D Geometry of cultural Artifacts using a synthetically trained Generative Model},
  author = {Thomas Pöllabauer and Julius Kühn and Jiayi Li and Arjan Kuijper},
  year   = {2024},
  eprint = {2402.08310},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.08310v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2402.08310.svg)](https://codeofpaper.com/paper/2402.08310)

Discussion