Kyle Genova

About Me

I'm a PhD student advised by Thomas Funkhouser in the Vision & Robotics Group at Princeton University. My work is at the intersection of computer vision and graphics, particularly in the areas of shape representation, differentiable rendering, scene understanding, and self-supervised deep learning.

Previously, I was an undergraduate in Computer Science at Cornell University, where I worked with David Williamson in the areas of theoretical computer science and combinatorial optimization.

Research

Local Deep Implicit Functions for 3D Shape
Kyle Genova, Forrester Cole, Avneesh Sud, Aaron Sarna, Thomas Funkhouser
CVPR 2020 (Oral Presentation) and arXiv. Formerly "Deep Structured Implicit Functions."
Project page, Video, and Code

CvxNet: Learnable Convex Decomposition
Boyang Deng, Kyle Genova, Soroosh Yazdani, Sofien Bouaziz, Geoffrey Hinton, Andrea Tagliasacchi
CVPR 2020 (Best Paper Nominee) and arXiv

Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation
Zeyu Wang, Klint Qinami, Ioannis Christos Karakozis, Kyle Genova, Prem Nair, Kenji Hata, Olga Russakovsky
CVPR 2020 and arXiv

Learning Shape Templates with Structured Implicit Functions
Kyle Genova, Forrester Cole, Daniel Vlasic, Aaron Sarna, William T. Freeman, Thomas Funkhouser
ICCV 2019 and arXiv
Code

Text-based Editing of Talking-head Video
Ohad Fried, Ayush Tewari, Michael Zollhöfer, Adam Finkelstein, Eli Schechtman, Dan B. Goldman, Kyle Genova, Zeyu Jin, Christian Theobalt, Maneesh Agrawala
SIGGRAPH 2019 and arXiv
Video (300K Views) and project page

Unsupervised Training for 3D Morphable Model Regression
Kyle Genova, Forrester Cole, Aaron Maschinot, Aaron Sarna, Daniel Vlasic, William T. Freeman
CVPR 2018 (Spotlight Presentation) and arXiv

Learning Where to Look: Data-Driven Viewpoint Set Selection for 3D Scenes
Kyle Genova, Manolis Savva, Angel X. Chang, Thomas Funkhouser
arXiv

An Experimental Evaluation of the Best-of-Many Christofides' Algorithm for the Traveling Salesman Problem
Kyle Genova and David P. Williamson
Algorithmica 2017 (Selected Publication), ESA 2015, and arXiv

Fellowships & Scholarships

NSF GRFP
National Science Foundation, 2018-2022

Gordon Y.S. Wu Fellowship in Engineering
Princeton Unversity, 2016-2021

Hatton Lovejoy Scholarship
Fuller E. Callaway Foundation, 2012-2016

Valtarese Foundation Scholarship
Valtarese Foundation, 2012

Awards

Class of 2021 Siebel Scholar
The Thomas and Stacey Siebel Foundation, 2020

Graduate Student Teaching Award
Princeton University, 2018

Phi Beta Kappa
Cornell University, 2015

Computing Research Association Oustanding Undergraduate Researcher Nomination
Cornell University, 2015

Internships

Ongoing
Google
Ph.D. Internship, Mountain View, CA, 2019-2020
Long-term internship in Machine Perception Group.

Learning Shape Templates with Structured Implicit Functions
Google
Ph.D. Internship, Cambridge, MA, 2018
Cambridge Vision Research Group, with Aaron Sarna and Bill Freeman

Unsupervised Training for 3D Morphable Model Regression
Google
Ph.D. Internship, Cambridge, MA, 2017
Cambridge Vision Research Group, with Forrester Cole and Bill Freeman

In-Memory K-Way Balanced Graph Partitioning
Google
Ph.D. Internship, New York, NY, 2016
NYC Algorithms Research Group, with Aaron Archer and Vahab Mirrokni

Reviewing

ECCV (2020 Top Reviewer)
CVPR (2020, 2019, 2018)
SIGGRAPH 2020
ICCV 2019
NeurIPS (2019 Top Reviewer)
SIGGRAPH ASIA 2019

Teaching

Princeton University, Assistant in Instruction
Computer Graphics, COS426 Spring 2018
Computer Vision, COS 429 Fall 2017

Cornell University, Teaching Assistant
Intro to Analysis of Algorithms, CS 4820 Spring 2016
Computer Graphics, CS 4620 Fall 2015
Data Structures and Functional Programming, CS 3110 Fall 2014