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Found 24 papers, 17 papers with code
Explorable Mesh Deformation Subspaces from Unstructured Generative Models
Deep generative models of 3D shapes often feature continuous latent spaces that can, in principle, be used to explore potential variations starting from a set of input shapes.
Generative Escher Meshes
We thus consider both the mesh's tile-shape and its texture as optimizable parameters, rendering the textured mesh via a differentiable renderer.
Neural Semantic Surface Maps
We present an automated technique for computing a map between two genus-zero shapes, which matches semantically corresponding regions to one another.
Neural Progressive Meshes
The recent proliferation of 3D content that can be consumed on hand-held devices necessitates efficient tools for transmitting large geometric data, e. g., 3D meshes, over the Internet.
Neural Face Rigging for Animating and Retargeting Facial Meshes in the Wild
We propose an end-to-end deep-learning approach for automatic rigging and retargeting of 3D models of human faces in the wild.
TextDeformer: Geometry Manipulation using Text Guidance
Our key observation is that Jacobians are a representation that favors smoother, large deformations, leading to a global relation between vertices and pixels, and avoiding localized noisy gradients.
DA Wand: Distortion-Aware Selection using Neural Mesh Parameterization
We present a neural technique for learning to select a local sub-region around a point which can be used for mesh parameterization.
PatchRD: Detail-Preserving Shape Completion by Learning Patch Retrieval and Deformation
Our key insight is to copy and deform patches from the partial input to complete missing regions.
Learning Joint Surface Atlases
We demonstrate that this improves the quality of the learned surface representation, as well as its consistency in a collection of related shapes.
Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshes
This paper introduces a framework designed to accurately predict piecewise linear mappings of arbitrary meshes via a neural network, enabling training and evaluating over heterogeneous collections of meshes that do not share a triangulation, as well as producing highly detail-preserving maps whose accuracy exceeds current state of the art.
Neural Convolutional Surfaces
Our pipeline and architecture are designed so that disentanglement of global geometry from local details is accomplished through optimization, in a completely unsupervised manner.
Learning Proximal Operators to Discover Multiple Optima
We present an end-to-end method to learn the proximal operator of a family of training problems so that multiple local minima can be quickly obtained from initial guesses by iterating the learned operator, emulating the proximal-point algorithm that has fast convergence.
Möbius Convolutions for Spherical CNNs
M\"obius transformations play an important role in both geometry and spherical image processing - they are the group of conformal automorphisms of 2D surfaces and the spherical equivalent of homographies.
Temporally-Consistent Surface Reconstruction using Metrically-Consistent Atlases
The key to making these correspondences semantically meaningful is to guarantee that the metric tensors computed at corresponding points are as similar as possible.
Ranked #1 on
Surface Reconstruction
on ANIM
Differentiable Surface Triangulation
Our method builds on the result that any 2D triangulation can be achieved by a suitably perturbed weighted Delaunay triangulation.
GLASS: Geometric Latent Augmentation for Shape Spaces
We investigate the problem of training generative models on a very sparse collection of 3D models.
Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases
We propose a method for the unsupervised reconstruction of a temporally-coherent sequence of surfaces from a sequence of time-evolving point clouds, yielding dense, semantically meaningful correspondences between all keyframes.
Neural Surface Maps
Maps are arguably one of the most fundamental concepts used to define and operate on manifold surfaces in differentiable geometry.
Joint Learning of 3D Shape Retrieval and Deformation
In fact, we use the embedding space to guide the shape pairs used to train the deformation module, so that it invests its capacity in learning deformations between meaningful shape pairs.
DECOR-GAN: 3D Shape Detailization by Conditional Refinement
During testing, a style code is fed into the generator to condition the refinement.
Learning Delaunay Surface Elements for Mesh Reconstruction
We leverage the properties of 2D Delaunay triangulations to construct a mesh from manifold surface elements.
Coupling Explicit and Implicit Surface Representations for Generative 3D Modeling
We propose a novel neural architecture for representing 3D surfaces, which harnesses two complementary shape representations: (i) an explicit representation via an atlas, i. e., embeddings of 2D domains into 3D; (ii) an implicit-function representation, i. e., a scalar function over the 3D volume, with its levels denoting surfaces.
Neural Subdivision
During inference, our method takes a coarse triangle mesh as input and recursively subdivides it to a finer geometry by applying the fixed topological updates of Loop Subdivision, but predicting vertex positions using a neural network conditioned on the local geometry of a patch.
Neural Cages for Detail-Preserving 3D Deformations
The goal of our method is to warp a source shape to match the general structure of a target shape, while preserving the surface details of the source.
