no code implementations • 25 Apr 2024 • Arman Maesumi, Dylan Hu, Krishi Saripalli, Vladimir G. Kim, Matthew Fisher, Sören Pirk, Daniel Ritchie
Procedural noise is a fundamental component of computer graphics pipelines, offering a flexible way to generate textures that exhibit "natural" random variation.
no code implementations • 11 Oct 2023 • Arman Maesumi, Paul Guerrero, Vladimir G. Kim, Matthew Fisher, Siddhartha Chaudhuri, Noam Aigerman, Daniel Ritchie
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.
1 code implementation • 22 Apr 2021 • Arman Maesumi, Mingkang Zhu, Yi Wang, Tianlong Chen, Zhangyang Wang, Chandrajit Bajaj
This paper presents a novel patch-based adversarial attack pipeline that trains adversarial patches on 3D human meshes.
1 code implementation • 4 Jul 2020 • Arman Maesumi
We have seen numerous machine learning methods tackle the game of chess over the years.
1 code implementation • 30 Apr 2018 • 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, and CA.
General Mathematics 60D05