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

April 2024 One journal paper accepted to SIGGRAPH 2024! ⛰️
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.

One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns

Arman Maesumi, Dylan Hu, Krishi Saripalli, Vladimir G. Kim, Matthew Fisher, Sören Pirk, Daniel Ritchie
ACM Transactions on Graphics (Proceedings of SIGGRAPH), 2024
[ project page / pdf (34mb) / arXiv / bibtex ]

We learn a denoising diffusion model that can produce multiple types of parametrized noise functions and blend between them spatially, even without spatially-varying training data.

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 (Conference Track)
[ 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.


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