Browsing by Author "Chaine, Raphaelle"

Now showing 1 - 3 of 3
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    Anatomy Changes and Virtual Restoration of Statues
    (The Eurographics Association, 2020) Fu, Tong; Chaine, Raphaelle; Digne, Julie; Spagnuolo, Michela and Melero, Francisco Javier
    Restoration of archaeological artefacts is an important task for cultural heritage preservation. However traditional restoration processes are difficult, costly and sometimes risky for the artefact itself, due to poor restoration choices for example. To avoid this, it is interesting to turn to virtual restoration, which allows to test restoration hypotheses, that can be later carried out on the real artefact. In this paper, we introduce a restoration framework for completing missing parts of archaeological statues, with a focus on human sculptures. Our approach proceeds by registering an anatomical model to a statue, identifying the missing parts. Compatible statues are then provided by the users and their poses are changed to match the broken statue, using a point-cloud specific skinning technique. The modified statues provide replacement parts which are blended in the original statue.
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    FAKIR: An Algorithm for Revealing the Anatomy and Pose of Statues from Raw Point Sets
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Fu, Tong; Chaine, Raphaelle; Digne, Julie; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
    3D acquisition of archaeological artefacts has become an essential part of cultural heritage research for preservation or restoration purpose. Statues, in particular, have been at the center of many projects. In this paper, we introduce a way to improve the understanding of acquired statues representing real or imaginary creatures by registering a simple and pliable articulated model to the raw point set data. Our approach performs a Forward And bacKward Iterative Registration (FAKIR) which proceeds joint by joint, needing only a few iterations to converge. We are thus able to detect the pose and elementary anatomy of sculptures, with possibly non realistic body proportions. By adapting our simple skeleton, our method can work on animals and imaginary creatures.
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    Volume Preserving Neural Shape Morphing
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Buonomo, Camille; Digne, Julie; Chaine, Raphaelle; Attene, Marco; Sellán, Silvia
    Shape interpolation is a long standing challenge of geometry processing. As it is ill-posed, shape interpolation methods always work under some hypothesis such as semantic part matching or least displacement. Among such constraints, volume preservation is one of the traditional animation principles. In this paper we propose a method to interpolate between shapes in arbitrary poses favoring volume and topology preservation. To do so, we rely on a level set representation of the shape and its advection by a velocity field through the level set equation, both shape representation and velocity fields being parameterized as neural networks. While divergence free velocity fields ensure volume and topology preservation, they are incompatible with the Eikonal constraint of signed distance functions. This leads us to introduce the notion of adaptive divergence velocity field, a construction compatible with the Eikonal equation with theoretical guarantee on the shape volume preservation. In the non constant volume setting, our method is still helpful to provide a natural morphing, by combining it with a parameterization of the volume change over time. We show experimentally that our method exhibits better volume preservation than other recent approaches, limits topological changes and preserves the structures of shapes better without landmark correspondences.