Mikaela Angelina Uy

I'm currently a Research Scientist at NVIDIA Toronto AI Lab led by Sanja Filder. I obtained my PhD at Stanford University advised by Leonidas Guibas. Broadly, my research interests are in 3D vision, geometry processing, graphics and machine learning. Specifically, I am interested in diving into different representations of 3D objects and scenes for various downstream tasks such as deformation, reconstruction, controllable generation and variation synthesis. I am particularly drawn to designing methods that connect classical techniques to learning-based approaches that are fundamentally-grounded and mathematically-inspired.

During my PhD, I was a research intern at Google in the Project Starline team, Adobe Research and Autodesk AI Lab. I was also fortunate to have also closely collaborated with Ke Li and Minhyuk Sung, whose advice have also guided me to grow, appreciate and develop my taste in research.

I am grateful to be a recipient of the 2023 Apple Scholars in AI/ML PhD Fellowship and the 2022 Snap Research Fellowship. I'm also selected as one of the EECS Rising Stars 2023.

Previously, I received my Bachelor's degree double majoring in Mathematics and Computer Science from the Hong Kong University of Science and Technology (HKUST) in 2017, and my Master's in Computing from the National University of Singapore in 2018. I then returned to HKUST as a Research Assistant for a year and had the pleasure of working with Sai-Kit Yeung, Binh-Son Hua and Duc Thanh Nguyen.

CV
mikacuy [at] gmail [dot] com
mikacuy [at] stanford [dot] edu
Google scholar
Github
Twitter

Also check out a feature article for Women in Computer Vision from RSIP Vision here.

Selected Publications

ProvNeRF: Modeling per Point Provenance in NeRFs as a Stochastic Process
George Kiyohiro Nakayama, Mikaela Angelina Uy, Yang You, Ke Li, Leonidas Guibas
Advances in Neural Information Processing Systems (NeurIPS), 2024
Project Page


MV2Cyl: Reconstructing 3D Extrusion Cylinders from Multi-View Images
Eunji Hong, Nguyen Minh Hieu, Mikaela Angelina Uy, Minhyuk Sung
Advances in Neural Information Processing Systems (NeurIPS), 2024
Paper


Dynamic Gaussian Marbles for Novel View Synthesis of Casual Monocular Videos
Colton Stearns, Adam Harley, Mikaela Angelina Uy, Florian Dubost, Federico Tombari, Gordon Wetzstein, Leonidas Guibas
SIGGRAPH Asia, 2024
Project Page Paper Code


NeRF Revisited: Fixing Quadrature Instability in Volume Rendering
Mikaela Angelina Uy, George Kiyohiro Nakayama, Guandao Yang, Rahul Krishna Thomas, Leonidas Guibas, Ke Li
Advances in Neural Information Processing Systems (NeurIPS), 2023
Project Page Paper Code Video Poster


DiffFacto: Controllable Part-Based 3D Point Cloud Generation with Cross Diffusion
George Kiyohiro Nakayama, Mikaela Angelina Uy, Jiahui Huang, Shi-Min Hu, Ke Li, Leonidas Guibas
IEEE International Conference on Computer Vision (ICCV), 2023
Project Page Paper Code Video


OptCtrlPoints: Optimizing Control Points for Biharmonic 3D Shape Deformation
Kunho Kim*, Mikaela Angelina Uy*, Despoina Paschalidou, Alec Jacobson, Leonidas Guibas, Minhyuk Sung
Pacific Graphics (Full Paper), 2023
Project Page Paper Code Video Slides


SCADE: NeRFs from Space Carving with Ambiguity-Aware Depth Estimates
Mikaela Angelina Uy, Ricardo Martin-Brualla, Leonidas Guibas, Ke Li
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023
Project Page Paper Code Video


PartNeRF: Generating Part-Aware Editable 3D Shapes without 3D Supervision
Konstaninos Tertikas, Despoina Paschalidou, Boxiao Pan, Jeong Joon Park, Mikaela Angelina Uy, Ioannis Emiris, Yannis Avrithis, Leonidas Guibas
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023
Project Page Paper


Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinders
Mikaela Angelina Uy*, Yen-yu Chang*, Minhyuk Sung, Purvi Goel, Joseph Lambourne, Tolga Birdal, Leonidas Guibas
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022
Project Page Paper Code Video Poster


Joint Learning of 3D Shape Retrieval and Deformation
Mikaela Angelina Uy, Vladimir G. Kim, Minhyuk Sung, Noam Aigerman, Siddhartha Chaudhuri, Leonidas Guibas
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021
Project Page Paper Code Video Poster


Deformation-Aware 3D Model Embedding and Retrieval
Mikaela Angelina Uy, Jingwei Huang, Minhyuk Sung, Tolga Birdal, Leonidas Guibas
European Conference on Computer Vision (ECCV), 2020
Project Page Paper Code 10-min Video 1-min Video


LCD: Learned Cross-Domain Descriptors for 2D-3D Matching
Quang-Hieu Pham, Mikaela Angelina Uy, Binh-Son Hua, Duc Thanh Nguyen, Gemma Roig, Sai-Kit Yeung
AAAI Conference on Artificial Intelligence (AAAI), 2020 (Oral)
Project Page Paper Code


Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data
Mikaela Angelina Uy, Quang-Hieu Pham, Binh-Son Hua, Duc Thanh Nguyen, Sai-Kit Yeung
IEEE International Conference on Computer Vision (ICCV), 2019 (Oral)
Project Page Paper Supplementary Code Poster

Invited Talks

  • Apple Machine Learning Research (MLR), September 7, 2023, Towards Controllable 3D Content Creation by Leveraging Geometric Priors
  • Google, July 12, 2023, NeRF Revisited: Fixing Quadrature Instability in Volume Rendering
  • SFU Visual Computing and Robotics (VCR) Seminar, June 26, 2023, Towards Controllable 3D Content Creation by Leveraging Geometric Priors
  • Structural and Compositional Learning on 3D Data, CVPR 2023 Workshop, June 18, 2023, Towards Controllable 3D Content Creation by Leveraging Geometric Priors
  • KAIST, January 9, 2023, SCADE: NeRFs from Space Carving with Ambiguity-Aware Depth Estimates
  • VinAI Seminar Series, July 22, 2022, Learning to Vary 3D Models for Universally Accessible 3D Content Creation
  • Brown Vision Computing Seminar, April 11, 2022, Learning to Vary 3D Models for Universally Accessible 3D Content Creation
  • Stanford G-Cafe, March 10, 2022, Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinders

Selected Awards

  • EECS Rising Stars, 2023
  • Apple Scholars in AI/ML PhD Fellowship, 2023
  • Snap Research Fellowship, 2022
  • Meta PhD Fellowship Finalist, 2023
  • School of Engineering Fellowship, Stanford University, 2019-2020
  • HKSAR Government Targeted Scholarship (Full 4-year university scholarship)
  • NUS Graduate Scholarship for ASEAN Nationals (Full masters scholarship)
  • Google Women Techmakers Scholarship, 2016
  • Epsilon Fund Award, HKUST Mathematics Department, 2017
  • International Mathematical Olympiad (IMO) Bronze Medalist, 2012, 2013
  • Philippine Mathematical Olympiad 1st runner-up, 2012, 2013

Work Experiences

Research Intern, Google

June 2023 - present
Mentors: Ke Li, Xuan Luo, Zhengqi Li

Research Intern, Project Starline, Google

June 2022 - January 2023
Mentors: Ke Li, Mirko Visontai

Research Intern, Autodesk AI Lab

June - September 2021
Mentors: Joseph Lambourne

Research Intern, Creative Intelligence Lab, Adobe Research

June - September 2020
Mentors: Vladimir G. Kim, Minhyuk Sung, Noam Aigerman, Siddhartha Chaudhuri


Research Assistant, Vision & Graphics Group, HKUST

September 2018 - June 2019
Mentors: Prof. Sai-Kit Yeung, Binh-Son Hua, Duc Thanh Nguyen

Teaching Experiences

  • Stanford CS 348n Guest Lecture, May 31, 2023, Neural Radiance Fields: Sparse View and Dynamic Scenes
  • Stanford CS 348n Guest Lecture, May 24, 2023, Continuous and Discrete Shape Edits/Deformations
  • Stanford CS 348n Guest Lecture, February 16, 2022, Neural Shape Variation and Generation
  • Teaching Assistant, Winter 2021, Computer Graphics: Geometric Modeling/Processing (CS 348a), Stanford University

Projects

Interpretable & Actionable Models using Attribute & Uncertainty Information

CS229 project, Autumn 2019
  • Deep-learning models can be difficult to understand and control intuitively due to the black-box nature of these models. However, such lack of interpretability and human actionability in the models’ decision processes make it difficult to trust these models in critical applications that affect the lives of people. We propose to alleviate these problems through the use of attribute and uncertainty models in deep networks.
Links: Report Poster

HKUST Robotics Team, Remotely Operated Vehicle (ROV) Subteam

Software Engineer, 2014-2015
  • Overall 3rd Place (Explorer Class) – 14th Annual MATE International Underwater Robotics Competition in St John’s, Newfoundland and Labrador, Canada
  • Asia Champion in 2015 MATE Asia Regional Underwater Robotics Competition
  • Built the main control software of the ROV, which operates with ROS and is controlled with an Xbox controller, and Qt GUI’s for the competition runs
  • The team is composed of 15 engineers who built and designed the ROV from scratch.
Links: Video

Underwater Object Detection

Undergraduate Thesis, HKUST
  • Advised by Prof. Chi-Keung Tang
  • Studied the performance of real-time object detection models, both using handcrafted features and deep learning networks, for underwater diver detection in robotics applications
Links: Poster

Hobbies and Interests

For most of my pre-university life, I was into competitive mathematics, with geometry being a favorite topic. I competed in various math competitions both local and abroad representing the Philippine Team. During my spare time back at home, I now train elementary and high school students for international math competitions. I was part of the training team of the 2017-2020 PH IMO team, and I led the PH team to a number of elementary math competitions.

I also enjoy playing soccer, frisbee and scuba diving. I was part of the HKUST Women's Soccer Team back in my senior year.

Academic Services

Reviewer: CVPR, ICCV, ECCV, SIGGRAPH, SIGGRAPH Asia, BMVC, 3DV, AAAI, TVCG, Eurographics, Neurips, ICLR