11 citations · 35 across the 35 of their papers we have counts for
10 papers · 1 filter
Residual Primitive Fitting of 3D Shapes with SuperFrusta
Aditya Ganeshan, Matheus Gadelha, Thibault Groueix +6
We introduce a framework for converting 3D shapes into compact and editable assemblies of analytic primitives, directly addressing the persistent trade-off between reconstruction f…
PoissonNet: A Local-Global Approach for Learning on Surfaces
Arman Maesumi, Tanish Makadia, Thibault Groueix +3
Many network architectures exist for learning on meshes, yet their constructions entail delicate trade-offs between difficulty learning high-frequency features, insufficient recept…
Escher Tile Deformation via Closed-Form Solution
Crane He Chen, Vladimir G. Kim
We present a real-time deformation method for Escher tiles -- interlocking organic forms that seamlessly tessellate the plane following symmetry rules. We formulate the problem as…
ART-DECO: Arbitrary Text Guidance for 3D Detailizer Construction
Qimin Chen, Yuezhi Yang, Wang Yifan +4
We introduce a 3D detailizer, a neural model which can instantaneously (in <1s) transform a coarse 3D shape proxy into a high-quality asset with detailed geometry and texture as gu…
GenVDM: Generating Vector Displacement Maps From a Single Image
Yuezhi Yang, Qimin Chen, Vladimir G. Kim +3
We introduce the first method for generating Vector Displacement Maps (VDMs): parameterized, detailed geometric stamps commonly used in 3D modeling. Given a single input image, our…
One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns
Arman Maesumi, Dylan Hu, Krishi Saripalli +4
Procedural noise is a fundamental component of computer graphics pipelines, offering a flexible way to generate textures that exhibit "natural" random variation. Many different typ…
MagicClay: Sculpting Meshes With Generative Neural Fields
Amir Barda, Vladimir G. Kim, Noam Aigerman +2
The recent developments in neural fields have brought phenomenal capabilities to the field of shape generation, but they lack crucial properties, such as incremental control - a fu…
Explorable Mesh Deformation Subspaces from Unstructured Generative Models
Arman Maesumi, Paul Guerrero, Vladimir G. Kim +4
Exploring variations of 3D shapes is a time-consuming process in traditional 3D modeling tools. Deep generative models of 3D shapes often feature continuous latent spaces that can,…
Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshes
Noam Aigerman, Kunal Gupta, Vladimir G. Kim +3
This paper introduces a framework designed to accurately predict piecewise linear mappings of arbitrary meshes via a neural network, enabling training and evaluating over heterogen…
The Shape Part Slot Machine: Contact-based Reasoning for Generating 3D Shapes from Parts
Kai Wang, Paul Guerrero, Vladimir Kim +3
We present the Shape Part Slot Machine, a new method for assembling novel 3D shapes from existing parts by performing contact-based reasoning. Our method represents each shape as a…