arman_maesumi@brown.edu

I am a third year Computer Science PhD student at Brown University, where I am advised by Professor Daniel Ritchie. My work is supported by the NSF Graduate Research Fellowship Program.

I received my BS in computer science from The University of Texas at Austin in 2021, where I did research with Professor Chandrajit Bajaj.

My research interests lie at the intersection of machine learning, computer graphics, and 3D computer vision. In my free time I enjoy creating 3D art, cooking, and playing chess.


profile photo
News

Aug 2023 One paper accepted to SIGGRAPH Asia 2023! 🦘
May 2023 I am returning to Adobe Research as a Research Scientist Intern.
May 2022 I'll be joining Adobe Research as a Research Scientist Intern.
April 2022 I was awarded the NSF Graduate Research Fellowship.
Article: "Five Brown CS Students And Alums Receive NSF Graduate Research Fellowships"
Sept. 2021 Started my PhD at Brown!

I am interested in the applications of machine learning methods for representing, synthesizing, and manipulating 3D objects and scenes.

Explorable Mesh Deformation Subspaces from Unstructured 3D Generative Models

Arman Maesumi, Paul Guerrero, Vladimir G. Kim, Matthew Fisher, Siddhartha Chaudhuri, Noam Aigerman, Daniel Ritchie
SIGGRAPH Asia, 2023
project page / pdf (30mb) / arXiv / bibtex

Given a set of landmark meshes, our method extracts a 2-d deformation subspace from a pretrained non-mesh 3-d generative model, which facilitates exploration of continuous variations between the landmark meshes.


Learning Transferable 3D Adversarial Cloaks for Deep Trained Detectors

Arman Maesumi*, Mingkang Zhu*, Yi Wang, Tianlong Chen, Zhangyang Wang, Chandrajit Bajaj
arXiv, 2021
pdf (8mb) / arXiv / bibtex

3D human meshes are cloaked from object detectors via adversarial texture maps, which are trained using differentiable rendering.

Triangle Inscribed-Triangle Picking
Arman Maesumi
The College Mathematics Journal, 2019
pdf (1mb) / journal / bibtex

The probability density function and moments (OEIS A279055) of the area of stochastically generated inscribed geometry are derived.

Preliminary findings were presented at TUMC 2017.

Renderings

In my free time, I enjoy creating 3D renderings and physical simulations using various software. More can be found here. The programs and tools that I use include: Blender, Cinema 4D, RealFlow, Vray, Octane, Arnold, Krakatoa, and more.

Click to see more


Template