10 citations · 18 across the 7 of their papers we have counts for
8 papers
Monte Carlo Steklov Operators for Large-Scale Geometry Processing in the Wild
Arman Maesumi, Tanish Makadia, Aruna Anderson +3
Intrinsic methods fill the default toolbox for geometry processing on meshes. Intrinsic operators, in particular the Laplacian, underlie methods that require invariance to isometry…
Matérn Noise for Triangulation-Agnostic Flow Matching on Meshes
Tianshu Kuai, Arman Maesumi, Daniel Ritchie +1
This paper tackles the task of learning to generate signals over triangle meshes in a triangulation-agnostic manner, meaning the trained model can be applied to different meshes an…
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…
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…
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,…
Learning Transferable 3D Adversarial Cloaks for Deep Trained Detectors
Arman Maesumi, Mingkang Zhu, Yi Wang +3
This paper presents a novel patch-based adversarial attack pipeline that trains adversarial patches on 3D human meshes. We sample triangular faces on a reference human mesh, and cr…
Playing Chess with Limited Look Ahead
Arman Maesumi
We have seen numerous machine learning methods tackle the game of chess over the years. However, one common element in these works is the necessity of a finely optimized look ahead…
Triangle Inscribed-Triangle Picking
Arman Maesumi
Given a triangle ABC, we derive the probability distribution function and the moments of the area of an inscribed triangle RST whose vertices are uniformly distributed on AB, BC, a…