DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer

Wenzheng Chen, Joey Litalien, Jun Gao, Zian Wang, Clément Fuji Tsang, Sameh Khamis, Or Litany, and Sanja Fidler
Neural Information Processing Systems (NeurIPS), December 2021

Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes

Towaki Takikawa, Joey Litalien, Kangxue Yin, Karsten Kreis, Charles Loop, Derek Nowrouzezahrai, Alec Jacobson, Morgan McGuire, and Sanja Fidler
Computer Vision and Pattern Recognition (CVPR), January 2021

Delayed Rejection Metropolis Light Transport

Damien Rioux-Lavoie, Joey Litalien, Adrien Gruson, Toshiya Hachisuka, and Derek Nowrouzezahrai
ACM Transactions on Graphics (Presented at SIGGRAPH), May 2020

Learning Visibility in Ray Space

Joey Litalien
Master's thesis (M.Eng.), 2018

Other Projects

Online Test Suite for Rendering Research

A web comparison tool for rendering research written in Python+JS, based on the online test suite with interactive viewer by Disney Research. Useful for assembling supplementary material (comparison metrics, convergence plots, etc.)

Noise2Noise PyTorch Implementation

Unofficial PyTorch implementation of Noise2Noise: Learning Image Restoration without Clean Data by Lehtinen et al., 2018

Reproducing L2HMC

ICLR 2018 Reproducibility Challenge for Generalizing Hamiltonian Monte Carlo with Neural Networks by Lévy et al.

CelebA GANs

Implementation of different generative adversarial networks in PyTorch for small CelebA face generation.