One-to-many Reconstruction of 3D Geometry of cultural Artifacts using a synthetically trained Generative Model
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
Description
CCS Concepts: Computing methodologies → Reconstruction; Supervised learning; Applied computing → Archaeology
@inproceedings{10.2312:gch.20231161,
booktitle = {Eurographics Workshop on Graphics and Cultural Heritage},
editor = {Bucciero, Alberto and Fanini, Bruno and Graf, Holger and Pescarin, Sofia and Rizvic, Selma},
title = {{One-to-many Reconstruction of 3D Geometry of cultural Artifacts using a synthetically trained Generative Model}},
author = {Pöllabauer, Thomas and Kühn, Julius and Li, Jiayi and Kuijper, Arjan},
year = {2023},
publisher = {The Eurographics Association},
ISSN = {2312-6124},
ISBN = {978-3-03868-217-2},
DOI = {10.2312/gch.20231161}
}