Title:	LatEd: A Geometric Latent Vector Editor
Authors:	Komar, Alexander Kammerer, Michael Barzegar Khalilsaraei, Saeedeh Augsdoerfer, Ursula
Citation:	WSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p.325-330.
Issue Date:	2024
Publisher:	Václav Skala - UNION Agency
Document type:	konferenční příspěvek conferenceObject
URI:	http://hdl.handle.net/11025/57406
ISSN:	2464–4625 (online) 2464–4617 (print)
Keywords:	neuronové sítě;GAN;signed distance fields
Keywords in different language:	neural networks;GAN;signed distance fields
Abstract in different language:	Using a neural network approach, a shape may be compressed to a one-dimensional vector, the so-called latent dimension or latent vector. This latent shape dimension is examined in this paper. This latent vector of a shape is used to identify the corresponding shape in a database. Two types of networks are evaluated in terms of lookup accuracy and reconstruction quality using a database of Lego pieces. Even with small training set a reasonable robustness to rotation and translation of the shapes was achieved. While a human can interpret uncompressed data just fine, the compressed values of the network might be cryptic and thus offer no insight regarding the uncompressed input. Therefore, we introduce a latent dimension editor which allows the user to examine the geometry content of the latent vector and its influence on the decoded shape. The latent vector editor enables the visual exploration of the latent vector, by making changes to the latent vector visible in real-time via a 3D visualization of the reconstructed object.
Rights:	© Václav Skala - UNION Agency
Appears in Collections:	WSCG 2024: Full Papers Proceedings

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