|
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. |
|
|
|
|
I am interested in the applications of machine learning methods for representing, synthesizing, and manipulating 3D objects and scenes. |
|
Arman Maesumi, Dylan Hu, Krishi Saripalli, Vladimir G. Kim, Matthew Fisher, Sören Pirk, Daniel Ritchie
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. |
|
Arman Maesumi, Paul Guerrero, Vladimir G. Kim, Matthew Fisher, Siddhartha Chaudhuri, Noam Aigerman, Daniel Ritchie
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. |
|
Arman Maesumi*, Mingkang Zhu*, Yi Wang, Tianlong Chen, Zhangyang Wang, Chandrajit Bajaj
3D human meshes are cloaked from object detectors via adversarial texture maps, which are trained using differentiable rendering. |
|
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. |
| |