TzuMao Litzli@ucsd.edu

I am an assistant professor at the CSE department of UCSD, working with awesome people at the Center for Visual Computing. I explore the connections between visual computing algorithms and modern datadriven methods and develop programming languages and systems for facilitating the exploration. I did a 2year postdoc with Jonathan RaganKelley at both MIT CSAIL and UC Berkeley. I did my Ph.D. in the computer graphics group at MIT CSAIL, advised by Frédo Durand. I received my B.S. and M.S. degrees in computer science and information engineering from National Taiwan University in 2011 and 2013, respectively, where I worked with YungYu Chuang at the Communication and Multimedia Lab.
Differentiable Rendering of Neural SDFs through Reparameterization
Sai Praveen Bangaru, Michaél Gharbi, TzuMao Li, Fujun Luan, Kalyan Sunkavalli, Miloš Hašan, Sai Bi, Zexiang Xu, Gilbert Bernstein, Frédo Durand SIGGRAPH Asia 2022 (conference track full paper) unbiased differentiable SDF rendering. concurrent with Vicini et al.'s work. some extra memory saving tricks are introduced. 

Designing Perceptual Puzzles by Differentiating Probabilistic Programs
Kartik Chandra, TzuMao Li, Joshua Tenenbaum, Jonathan RaganKelley SIGGRAPH 2022 (conference track full paper) synthesizing optical illusion by differentiating through Bayesian vision models 

Searching for Fast Demosaicking Algorithms
Karima Ma, Michael Gharbi, Andrew Adams, Shoaib Kamil, TzuMao Li, Connelly Barnes, Jonathan RaganKelley ACM Transaction on Graphics (Presented at SIGGRAPH 2022) systematically exploring the Pareto frontier of efficient and highquality datadriven image processing algorithms 

Efficient Automatic Scheduling of Imaging and Vision Pipelines for the GPU
Luke Anderson, Andrew Adams, Karima Ma, TzuMao Li, Tian Jin, Jonathan RaganKelley Proceedings of the ACM on Programming Languages (OOPSLA 2021) a scalable and datadriven Halide autoscheduler that can process large pipelines and output GPU schedules 

Learning to Cluster for Rendering with Many Lights
YuChen Wang, YuTing Wu, TzuMao Li, YungYu Chuang ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2021) adapting both the clustering and sampling distribution in lightcuts rendering using a datadriven method built on reinforcement lightcuts learning 

MultiResolution Shared Representative Filtering for RealTime Depth Completion
YuTing Wu, TzuMao Li, IChao Shen, HongShiang Lin, YungYu Chuang HighPerformance Graphics (HPG) 2021 fast samplingbased cross bilateral filtering for depth completion with large missing regions 

Systematically Differentiating Parametric Discontinuities
Sai Praveen Bangaru*, Jesse Michel*, Kevin Mu, Gilbert Bernstein, TzuMao Li, Jonathan RaganKelley *equal contribution ACM Transactions on Graphics (Proceedings of SIGGRAPH 2021) a programming language perspective on the automatic differention of integrals with discontinuous integrands, and its applications in physics and rendering 

Unbiased WarpedArea Sampling for Differentiable Rendering
Sai Praveen Bangaru, TzuMao Li, Frédo Durand ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2020) solving the differentiable rendering boundary integral with standard area sampling in an unbiased way using divergence theorem 

Differentiable Vector Graphics Rasterization for Editing and Learning
TzuMao Li, Michal Lukáč, Michaël Gharbi, Jonathan RaganKelley ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2020) design choices and applications of differentiable vector graphics rasterization 

PhysicsBased Differentiable Rendering: A Comprehensive Introduction
Shuang Zhao, Wenzel Jakob, TzuMao Li SIGGRAPH 2020 Course our differentiable rendering tutorial! 

DiffTaichi: Differentiable Programming for Physical Simulation
Yuanming Hu, Luke Anderson, TzuMao Li, Qi Sun, Nathan Carr, Jonathan RaganKelley, Frédo Durand International Conference on Learning Representation (ICLR) 2020 automatic differentiated Taichi and applications in modelbased reinforcement learning 

Taichi: A Language for HighPerformance Computation on Spatially Sparse Data Structures
Yuanming Hu, TzuMao Li, Luke Anderson, Jonathan RaganKelley, Frédo Durand ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2019) a dataoriented compiler that decouples hierarchical sparse data structures design from computation 

Learning to Optimize Halide with Tree Search and Random Programs
Andrew Adams, Karima Ma, Luke Anderson, Riyadh Baghdadi, TzuMao Li, Michaël Gharbi, Benoit Steiner, Steven Johnson, Kayvon Fatahalian, Frédo Durand, Jonathan RaganKelley ACM Transactions on Graphics (Proceedings of SIGGRAPH 2019) first Halide autoscheduler that produces faster code comparing to human experts on average 

Samplebased Monte Carlo Denoising using a KernelSplatting Network
Michaël Gharbi, TzuMao Li, Miika Aittala, Jaakko Lehtinen, Frédo Durand ACM Transactions on Graphics (Proceedings of SIGGRAPH 2019). permutation invariant mapping from Monte Carlo samples to an image through splatting 

Differentiable Visual Computing [slides (Keynote)] [slides (Powerpoint)]
TzuMao Li MIT PhD Dissertation ACM SIGGRAPH 2020 Outstanding Doctoral Dissertation Award (announcement) a coherent view of my PhD research, with some new discussions regarding previous work, and some background reviews 

Inverse Path Tracing for Joint Material and Lighting Estimation
Dejan Azinović, TzuMao Li, Anton Kaplanyan, Matthias Nießner Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (oral presentation) applying differentiable rendering for material and lighting reconstruction 

Differentiable Monte Carlo Ray Tracing through Edge Sampling
TzuMao Li, Miika Aittala, Frédo Durand, Jaakko Lehtinen ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2018) computing gradients of the light transport equation through an explicit sampling of Dirac delta functions on triangle edges 

Differentiable Programming for Image Processing and Deep Learning in Halide
TzuMao Li, Michaël Gharbi, Andrew Adams, Frédo Durand, Jonathan RaganKelley ACM Transactions on Graphics (Proceedings of SIGGRAPH 2018) Halide meets automatic differentiation, and a new way to think about datadriven image processing 

Aether: An Embedded Domain Specific Sampling Language for Monte Carlo Rendering
Luke Anderson, TzuMao Li, Jaakko Lehtinen, Frédo Durand ACM Transactions on Graphics (Proceedings of SIGGRAPH 2017) a programming language for Monte Carlo rendering that automatically computes the probability density of a light path sample 

Anisotropic Gaussian Mutations for Metropolis Light Transport through HessianHamiltonian Dynamics
TzuMao Li, Jaakko Lehtinen, Ravi Ramamoorthi, Wenzel Jakob, Frédo Durand ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2015) a Metropolis light transport algorithm that makes use of automatically differentiated Hessian matrix of light path contribution 

DualMatrix Sampling for Scalable Translucent Material Rendering
YuTing Wu, TzuMao Li, YuHsun Lin, and YungYu Chuang IEEE Transactions on Visualization and Computer Graphics (TVCG), 2015 subsurface scattering with manylights using matrix sampling 

SUREbased Optimization for Adaptive Sampling and Reconstruction
TzuMao Li, YuTing Wu, YungYu Chuang ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2012) Stein's unbiased risk estimator for sampling and denoising in Monte Carlo rendering 
lajolla
An educational renderer for UCSD CSE 272 

Deriving Radiative Backpropagation using the Recursive Form of Path Tracing
A short note about the paper "Radiative Backpropagation: An Adjoint Method for LightningFast Differentiable Rendering". I show that you can derive a similar algorithm using traditional automatic differentiation. 

diffvg
A differentiable vector graphics rasterizer with PyTorch and Tensorflow interfaces. 

redner
A differentiable Monte Carlo ray tracer with PyTorch and Tensorflow interfaces. 

Graphics bibtex
A mega bibtex file containing many graphicsrelated literatures. 

Joint Stein’s Unbiased Risk Estimation for Adaptive Sampling and Reconstruction
A short note on a generalized formulation of our SUREbased rendering method. 

dpt
My prototypical renderer. 

smallgdpt
GradientDomain Path Tracing in ~450 lines. 