Search Results for author: Tzu-Mao Li

Found 13 papers, 6 papers with code

Differentiable Monte Carlo Ray Tracing through Edge Sampling

1 code implementation SIGGRAPH 2018 Tzu-Mao Li, Miika Aittala, Frédo Durand, Jaakko Lehtinen

We introduce a general-purpose differentiable ray tracer, which, to our knowledge, is the first comprehensive solution that is able to compute derivatives of scalar functions over a rendered image with respect to arbitrary scene parameters such as camera pose, scene geometry, materials, and lighting parameters.

Inverse Rendering

Inverse Path Tracing for Joint Material and Lighting Estimation

no code implementations17 Mar 2019 Dejan Azinović, Tzu-Mao Li, Anton Kaplanyan, Matthias Nießner

We introduce Inverse Path Tracing, a novel approach to jointly estimate the material properties of objects and light sources in indoor scenes by using an invertible light transport simulation.

Lighting Estimation Retrieval

Differentiable Visual Computing

no code implementations27 Apr 2019 Tzu-Mao Li

Simulating light transport in the presence of multi-bounce glossy effects and motion in 3D rendering is challenging due to the hard-to-sample high-contribution areas.

DiffTaichi: Differentiable Programming for Physical Simulation

2 code implementations ICLR 2020 Yuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun, Nathan Carr, Jonathan Ragan-Kelley, Frédo Durand

We present DiffTaichi, a new differentiable programming language tailored for building high-performance differentiable physical simulators.

Physical Simulations

Differentiable Vector Graphics Rasterization for Editing and Learning

1 code implementation ACM Transactions on Graphics 2020 Tzu-Mao Li, Michal Lukáč, Michaël Gharbi, Jonathan Ragan-Kelley

We introduce a differentiable rasterizer that bridges the vector graphics and raster image domains, enabling powerful raster-based loss functions, optimization procedures, and machine learning techniques to edit and generate vector content.

Vector Graphics

Adversarial examples within the training distribution: A widespread challenge

1 code implementation30 Jun 2021 Spandan Madan, Tomotake Sasaki, Hanspeter Pfister, Tzu-Mao Li, Xavier Boix

This result provides evidence supporting theories attributing adversarial examples to the proximity of data to ground-truth class boundaries, and calls into question other theories which do not account for this more stringent definition of adversarial attacks.

Object Recognition Open-Ended Question Answering

Designing Perceptual Puzzles by Differentiating Probabilistic Programs

no code implementations26 Apr 2022 Kartik Chandra, Tzu-Mao Li, Joshua Tenenbaum, Jonathan Ragan-Kelley

We design new visual illusions by finding "adversarial examples" for principled models of human perception -- specifically, for probabilistic models, which treat vision as Bayesian inference.

Color Constancy Probabilistic Programming

Differentiable Rendering of Neural SDFs through Reparameterization

no code implementations10 Jun 2022 Sai Praveen Bangaru, Michaël Gharbi, Tzu-Mao Li, Fujun Luan, Kalyan Sunkavalli, Miloš Hašan, Sai Bi, Zexiang Xu, Gilbert Bernstein, Frédo Durand

Our method leverages the distance to surface encoded in an SDF and uses quadrature on sphere tracer points to compute this warping function.

Inverse Rendering

Neural Free-Viewpoint Relighting for Glossy Indirect Illumination

no code implementations12 Jul 2023 Nithin Raghavan, Yan Xiao, Kai-En Lin, Tiancheng Sun, Sai Bi, Zexiang Xu, Tzu-Mao Li, Ravi Ramamoorthi

In this paper, we demonstrate a hybrid neural-wavelet PRT solution to high-frequency indirect illumination, including glossy reflection, for relighting with changing view.

Tensor Decomposition

Differentiable Visual Computing for Inverse Problems and Machine Learning

no code implementations21 Nov 2023 Andrew Spielberg, Fangcheng Zhong, Konstantinos Rematas, Krishna Murthy Jatavallabhula, Cengiz Oztireli, Tzu-Mao Li, Derek Nowrouzezahrai

This approach is predicated by neural network differentiability, the requirement that analytic derivatives of a given problem's task metric can be computed with respect to neural network's parameters.

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