Princeton University
vivienn princeton dot edu
I am currently a fourth year PhD student in Computer Science at Princeton University, where I am advised by Professor Szymon Rusinkiewicz! I received my BA ('19) in CS and MS ('20) in EECS at UC Berkeley. At Berkeley, I was advised by Professor Ren Ng. I was also extremely fortunate to be mentored by Cecilia Zhang and Shiry Ginosar at BAIR. My research interests are in computer graphics, computer vision, and machine learning; I'm particularly interested in problems related to the digital humanities, computational photography, human perception, and creative applications. I'm also especially in working on preserving, studying, and visualizing cultural heritage and natural history. Aside from these, I'm passionate about teaching, STEM education and mental health advocacy.
Publications and Projects
Region-Aware Simplification and Stylization of 3D Line Drawings
Vivien Nguyen, Matthew Fisher, Aaron Hertzmann, Szymon Rusinkiewicz
Shape-conveying line drawings generated from 3D models normally create closed regions in image space. These lines and regions can be stylized to mimic various artistic styles, but for complex objects, the extracted topology is unnecessarily dense, leading to unappealing and unnatural results under stylization. Prior works typically simplify line drawings without considering the regions between them, and lines and regions are stylized separately, then composited together, resulting in unintended inconsistencies. We present a method for joint simplification of lines and regions simultaneously that penalizes large changes to region structure, while keeping regions closed. This feature enables region stylization that remains consistent with the outline curves and underlying 3D geometry.
Cleaning and Structuring the Label Space of the iMet Collection 2020
Vivien Nguyen, Sunnie S.Y. Kim (Equal Contribution)
The iMet 2020 dataset is a valuable resource in the space of fine-grained art attribution recognition, but we believe it has yet to reach its true potential. We document the unique properties of the dataset and observe that many of the attribute labels are noisy, more than is implied by the dataset description. Oftentimes, there are also semantic relationships between the labels (e.g., identical, mutual exclusion, subsumption, overlap with uncertainty) which we believe are underutilized. We propose an approach to cleaning and structuring the iMet 2020 labels, and discuss the implications and value of doing so. Further, we demonstrate the benefits of our proposed approach through several experiments. Our code and cleaned labels are available at this https URL.
Synthetic Defocus and Look-Ahead Autofocus for Casual Videography
Xuaner Zhang, Kevin Matzen, Vivien Nguyen, Dillon Yao, You Zhang, Ren Ng
In cinema, large camera lenses create beautiful shallow depth of field (DOF), but make focusing difficult and expensive. Accurate cinema focus usually relies on a script and a person to control focus in realtime. Casual videographers often crave cinematic focus, but fail to achieve it. We either sacrifice shallow DOF, as in smartphone videos; or we struggle to deliver accurate focus, as in videos from larger cameras. This paper is about a new approach in the pursuit of cinematic focus for casual videography. We present a system that synthetically renders refocusable video from a deep DOF video shot with a smartphone, and analyzes future video frames to deliver context-aware autofocus for the current frame.
Teaching
Course | Description | Semester |
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COS 426 (Princeton) | Computer Graphics (Grad TA) | Spring 2022 |
COS 429 (Princeton) | Computer Vision (Grad TA) | Fall 2021 |
CS 184 (Berkeley) | Computer Graphics and Image Processing (Co-Instructor) | Summer 2020 (first summer offering!) |
CS 184 (Berkeley) | Computer Graphics and Image Processing (GSI) | Spring 2020 |
CS 184 (Berkeley) | Computer Graphics and Image Processing (Head uGSI) | Spring 2019 |
CS 168 (Berkeley) | Networking and Internet Architecture (uGSI) | Fall 2018 |
CS 184 (Berkeley) | Computer Graphics and Image Processing (uGSI) | Spring 2018 |
Work Experience
Where | What | When |
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Adobe | Research Intern Non-photorealistic rendering, advised by Aaron Hertzmann and Matt Fisher. |
May - Aug 2021 |
NVIDIA | Graphics and Content Developer Engineer Intern Tone mapping in RTX, render data collection pipeline, CUDA-Vulkan interop |
May - Aug 2019 |
NVIDIA | Software Tools for Hardware Infrastructure Intern Building tools for debugging hardware performance, profiling graphics pipelines |
May - Aug 2018 |