I am a passionate researcher who seeks creative solutions to new problems and innovative applications. My research is focused on developing artificial intelligence for geometry processing, spanning the fields of computer vision, computer graphics, and machine learning.
MeshUp deforms a 3D mesh into multiple concepts specified by text, images, or other meshes, while enabling to control the strength and location of their manifestation.
We build a procedural program with an interpretable parameter space to recover a high quality and easily manipulated mesh from a point cloud or a sketch input.
We use pure correspondence learning and direct refinement optimization to predict a highly accurate scene flow while using a fraction of the training data.
We apply a low frequency perturbation in the spectral shape domain to alter the reconstruction by a victim mesh autoencoder to a desired output geometry.
We use latent similarity and the point coordinates themselves to construct one point cloud by the other and achive an accurate dense matching with a small training data amount and without any correspondence supervision.
We propose a data-driven sampling approach for 3D point clouds that selects the most suitable subset of points for a downstream task.
Teaching
Topics in Machine Learning: 3D Geometry Processing and Computer Vision with Deep Neural Networks
CMSC 35401
Winter 2024
University of Chicago Teaching Assistant
Computer Vision
0510-6251-01
Fall 2024
Tel Aviv University Lead Lecturer
Service
Meta Reviewer:
International Program Committee, SGP 2025
Area Chair, WACV 2025
Judge, SIGGRAPH 2024 Student Research Competition
Poster Juror, SIGGRAPH 2024 Poster Program