10 papers
Intrinsic and Triangulation-Agnostic Attention: A Simple and Powerful Approach for Learning on Meshes
Ashwath Shetty, Zihan Zhu, Soeren Pirk +1
This work proposes an adaptation of the attention mechanism for triangle meshes. The core observation is that endowing the attention mechanism with critical properties for learning…
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…
Sketch-guided Cage-based 3D Gaussian Splatting Deformation
Tianhao Xie, Noam Aigerman, Eugene Belilovsky +1
3D Gaussian Splatting (GS) is one of the most promising novel 3D representations that has received great interest in computer graphics and computer vision. While various systems ha…
Differentiation Through Black-Box Quadratic Programming Solvers
Connor W. Magoon, Fengyu Yang, Noam Aigerman +1
Differentiable optimization has attracted significant research interest, particularly for quadratic programming (QP). Existing approaches for differentiating the solution of a QP w…
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…
Neural Kinematic Bases for Fluids
Yibo Liu, Zhixin Fang, Sune Darkner +4
We propose mesh-free fluid simulations that exploit a kinematic neural basis for velocity fields represented by an MLP. We design a set of losses that ensures that these neural bas…