Oct 11, 2023 · Explorable Mesh Deformation Subspaces from Unstructured Generative Models. Authors:Arman Maesumi, Paul Guerrero, Vladimir G. Kim, Matthew Fisher ...

To solve this problem, we introduce a deformation module that interprets an interpolation through the latent space of G as a. ow on a mesh's vertices, ...

We demonstrate our shape-to-shape deformation results against ShapeFlow, whereby we take random source and target meshes, and deform between them continuously.

Dec 11, 2023 · We then show how to turn the variations in this subspace into deformation fields, to transfer those variations to high-quality meshes for the ...

To address the challenge of smoothly deforming complex geometry, several papers use machine learning to infer classical low-dimensional controls. Yifan et al. [ ...

We find the optimal switch point in a subsection of the deformation sequence centered around t = 0.5, i.e. we do not take a switch point to be, say, t = 0.01, ...

We show that such deformation flows can be trivially applied to the input shape, resulting in a novel deformed version of the input without losing detail ...

Explorable Mesh Deformation Subspaces from Unstructured Generative Models · no ... TutteNet: Injective 3D Deformations by Composition of 2D Mesh Deformations.

Explorable Mesh Deformation Subspaces from Unstructured Generative Models ... Deep generative models of 3D shapes often feature continuous latent spaces that can, ...

Co-authors ; Explorable Mesh Deformation Subspaces from Unstructured 3D Generative Models. A Maesumi, P Guerrero, VG Kim, M Fisher, S Chaudhuri, N Aigerman, ...