Clément Jambon 🚴‍♂️

I am currently visiting the 3D Vision Lab of SNU 🇰🇷 for my Master's Thesis supervised by Prof. Young Min Kim and Prof. Olga Sorkine-Hornung. I recently completed a 4.5 months research internship at NVIDIA Research under the supervision of Thomas Müller and Merlin Nimier-David. In Fall 2024, I will join the PhD program of MIT EECS under the supervision of Prof. Mina Konaković Luković.

I am studying at ETH Zürich🇨🇭, where I follow a master of science. Among the Computer Science Department, I am majoring in Visual and Interactive Computing and minoring in Machine Learning. Additionally, I recently completed a postgraduate engineering degree at École polytechnique in Paris 🎓.

In Summer 2022, I had the incredible opportunity to take part in a five-month research adventure at Inria Sophia-Antipolis among the GraphDeco team. Supervised by George Drettakis and kindly advised by Georgios Kopanas, Stavros Diolatzis and Thomas Leimkühler, I mostly focused on point-based neural rendering and Neural Radiance Fields a.k.a. NeRF 🔫. This resulted in a contribution to Neural Point Catacaustics released at SIGGRAPH ASIA 2022 and NeRFshop that I presented in 2023 at I3D 👨‍🏫.

I am interested in Computer Graphics and its connections to machine learning. Most notably, I am currently focusing on high-performance, generalizable, and manipulable neural rendering and generation techniques. Some of my recent interests also include physically-based rendering, generative modeling, natural language processing, formal languages and optimal transport 🖥️.

In my free time, I enjoy hiking on ~~Swiss~~ Korean mountains ⛰️, cycling up (and down) steep hills 🚴, running with a compass and a map 🗺️, swimming in freezing lakes and rivers 🏊🏻, reading books and research papers 📖, watching international movies you've probably never heard of 🎥, and first and foremost talking and socializing 👋. So if you want to chat, I'm your man!

Email / Scholar / CV / Twitter / Github / LinkedIn

Education

MIT
Computer Science and Artificial Intelligence Laboratory
PhD program
Supervised by Prof. Mina Konaković Luković in the Algorithmic Design Group
Soon

ETH Zürich
Department of Computer Science
Master of Science
Major in Visual and Interactive Computing - Minor in Machine Learning
September 2022 - current

École polytechnique
Department of Computer Science
Postgraduate engineering degree
Major in Image, Vision and Learning
September 2019 - August 2023

Publications

NeRFshop: Interactive Editing of Neural Radiance Fields (I3D 2023)
Clément Jambon, Bernhard Kerbl, Georgios Kopanas, Stavros Diolatzis, Thomas Leimkühler, George Drettakis
NeRFshop is a novel end-to-end method that allows users to interactively select and deform objects through cage-based transformations. Once complete, edits can be collapsed and saved as a portable NeRF representation through a distillation process.
Project page | Paper | Video | Talk | Slides

Neural Point Catacaustics for Novel-View Synthesis of Reflections (SIGGRAPH Asia 2022)
Georgios Kopanas, Thomas Leimkühler, Gilles Reiner, Clément Jambon, George Drettakis
We introduce a new point-based representation to compute Neural Point Catacaustics allowing novel-view synthesis of scenes with curved reflectors, from a set of casually-captured input.
Project page | Paper | Video

Experience

NVIDIA
Research Intern
Supervisors: Thomas Müller and Merlin Nimier-David
October 2023 - February 2024

Inria - GraphDeco Team
Research Intern
Supervisor: George Drettakis
March 2022 - August 2022

Wemap
Computer Vision Engineer (Internship)
Supervisor: Thibaud Michel
June 2021 - August 2021

Institute of Technology of Cambodia
Teacher and Research Assistant (Internship)
October 2019 - March 2020

Projects

Improved 3D scene reconstruction with diffusion models (ETH Zürich)
Semester project supervised by Silvan Weder among the Computer Vision and Geometry Lab
During 5 months, I investigated large-scale 2D diffusion models in order to perform "neural extrapolations" of partial NeRF scenes. To tackle this problem, I mostly focused on the following state-of-the-art techniques: Feature Fields, Score Distillation Sampling (SDS) and feature-guided conditioning of diffusion models.
Report (71.7 MB) | Low-res report (9.7 MB)

Game Theory for Image Segmentation (ETH Zürich)
For the block course Controversies in Game Theory IX: Cooperative and Non-Cooperative Game Theory, I reproduced results from two previous works and implemented from scratch a segmentation algorithm based on game theory and evolutionary dynamics. Additionally, I investigated the use of DINO features for semantic segmentation.
Code | Report

Computer Graphics 2022: Rendering Competition (ETH Zürich)
Honorable Mention of the jury
For the Computer Graphics 2022 Rendering Competition at ETH Zürich, Marius Debussche and I implemented a path tracer with complex features including spectral rendering, subsurface scattering, an advanced camera model and participating media.