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2023
Conference Paper
Title
One-to-many Reconstruction of 3D Geometry of cultural Artifacts using a synthetically trained Generative Model
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.
reconstruct potential appearances of lost artifacts.
Open Access
Rights
CC BY 4.0: Creative Commons Attribution
Language
English
Keyword(s)
Branche: Cultural und Creative Economy
Research Line: Computer graphics (CG)
Research Line: Computer vision (CV)
Research Line: Human computer interaction (HCI)
Research Line: Machine learning (ML)
LTA: Scalable architectures for massive data sets
LTA: Machine intelligence, algorithms, and data structures (incl. semantics)
LTA: Generation, capture, processing, and output of images and 3D models
Machine learning
Interactive Machine learning
Artificial intelligence (AI)
Computer vision
Cultural heritage