Browsing by Author "Weinmann, Michael"

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    Portrait2Bust: DualStyleGAN-based Portrait Image Stylization Based on Bust Sculpture Images
    (The Eurographics Association, 2023) Sinha, Saptarshi Neil; Weinmann, Michael; Bucciero, Alberto; Fanini, Bruno; Graf, Holger; Pescarin, Sofia; Rizvic, Selma
    In cultural heritage, portrait paintings and busts are special genres of artworks which are used to show the appearance and expression of a human subject. The purpose of such artwork is to serve as remembrance of the person who is depicted in that portrait or bust. The bust can moreover serve as a 3D representation of a portrait painting. Therefore, it would be interesting to stylize a portrait painting based on a specific bust, i.e. the generation of a 2D image of a bust corresponding to the person depicted in the portrait image. In this paper, we analyze and discuss the stylization of portrait paintings and photographs of human faces with busts using a deep learning based style transfer approach. To capture the characteristics in the appearance of busts, we created a novel dataset of busts and used DualStyleGAN for the use cases of stylizing portrait paintings and stylizing human faces based on our novel bust style. Our experiments show the potential of this approach. Stylizing human faces as busts might not only be appealing to experts that might save time and effort for generating an initial stylization to refine later on, but also increase the engagement of novice users and exhibition visitors with cultural heritage.
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    Prototyping Care: Two Case Studies
    (The Eurographics Association, 2023) Clay, Arthur; Trumpy, Giorgio; Weinmann, Michael; Wetzel, Richard; Bucciero, Alberto; Fanini, Bruno; Graf, Holger; Pescarin, Sofia; Rizvic, Selma
    To enable a richer presentation of cultural heritage and its needs, a shift in how artworks are exhibited is necessary. This paper explores two case studies that highlight the significant role of reproductions in showcasing restoration processes and associated technologies. This approach raises awareness about concepts of care and authenticity and their impact. It goes beyond merely displaying restored digital images that fail to capture the true state of the artworks or the artist's original intent. To achieve this, we propose employing glass layers or lenticular print technology, allowing a restored version of the artwork's original state while maintaining the ability to view the original artwork and restoration process separately.
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    Temporal Upsampling of Point Cloud Sequences by Optimal Transport for Plant Growth Visualization
    (© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Golla, Tim; Kneiphof, Tom; Kuhlmann, Heiner; Weinmann, Michael; Klein, Reinhard; Benes, Bedrich and Hauser, Helwig
    Plant growth visualization from a series of 3D scanner measurements is a challenging task. Time intervals between successive measurements are typically too large to allow a smooth animation of the growth process. Therefore, obtaining a smooth animation of the plant growth process requires a temporal upsampling of the point cloud sequence in order to obtain approximations of the intermediate states between successive measurements. Additionally, there are suddenly arising structural changes due to the occurrence of new plant parts such as new branches or leaves. We present a novel method that addresses these challenges via semantic segmentation and the generation of a segment hierarchy per scan, the matching of the hierarchical representations of successive scans and the segment‐wise computation of optimal transport. The transport problems' solutions yield the information required for a realistic temporal upsampling, which is generated in real time. Thereby, our method does not require shape templates, good correspondences or huge databases of examples. Newly grown and decayed parts of the plant are detected as unmatched segments and are handled by identifying corresponding bifurcation points and introducing virtual segments in the previous, respectively successive time step. Our method allows the generation of realistic upsampled growth animations with moderate computational effort.