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presentation/modules/motivation.tex
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@ -1,8 +1,8 @@
\section{Motivation - why differentiable rendering is important}
\section{Motivation~-~why differentiable rendering is important}
\begin{frame}
\centering
\Huge
Motivation - why differentiable rendering is important
Motivation~-~why differentiable rendering is important
\end{frame}
\begin{frame}{Importance of differentiable rendering}
\begin{block}{Examples for Applications}
@ -36,7 +36,7 @@ with Differentiable Monte Carlo Raytracing [\cite{ACM:inverse_rendering}]\\
\end{frame}
\begin{frame}{Inverse rendering - current example}
\begin{frame}{Inverse rendering~-~current example}
\centering
\includemedia[
width=0.62\linewidth,height=0.35\linewidth,
@ -52,7 +52,7 @@ with Differentiable Monte Carlo Raytracing [\cite{ACM:inverse_rendering}]\\
]{}{VPlayer.swf}
\\
Source: \cite{ACM:inverse_rendering_signed_distance_function}
Source:~\cite{ACM:inverse_rendering_signed_distance_function}
\end{frame}
\subsection{Adversarial image generation}
\begin{frame}{Adversarial image generation}
@ -63,13 +63,13 @@ with Differentiable Monte Carlo Raytracing [\cite{ACM:inverse_rendering}]\\
$\implies$ Given a set of labels and a set of data, assign a label to each element in the dataset
\item Labeled data is needed to train classifier network
\end{itemize}
\pause
\pause{}
\vspace{15mm}
Image source: Auth0, \href{https://auth0.com/blog/captcha-can-ruin-your-ux-here-s-how-to-use-it-right/}{CAPTCHA Can Ruin Your UX. Here’s How to Use it Right}
\end{minipage}
\begin{minipage}{0.5\linewidth}
\centering
\includegraphics[width=0.5\linewidth]{presentation/img/recaptcha_example.png}
\includegraphics[width=0.5\linewidth]{img/recaptcha_example.png}
\end{minipage}
\end{center}
\end{frame}
@ -77,23 +77,23 @@ with Differentiable Monte Carlo Raytracing [\cite{ACM:inverse_rendering}]\\
\begin{itemize}
\item Problem: Labeling training data is tedious\\
$\implies$ We want to automatically generate training data
\item One solution: Generative adversarial networks. (e.g. AutoGAN [\cite{DBLP:AutoGAN}])\\
$\implies$ Impossible to make semantic changes to the image (e.g. lighting)
\item One solution: Generative adversarial networks (e.g. AutoGAN [\cite{DBLP:AutoGAN}]).\\
$\implies$ Impossible to make semantic changes to the image
\item Different solution: Use differentiable raytracing\\
$\implies$ Scene parameters can be manipulated
\end{itemize}
\end{frame}
\begin{frame}{Adversarial image generation - example [\cite{DBLP:journals/corr/abs-1910-00727}]}
\begin{frame}{Adversarial image generation~-~example [\cite{DBLP:journals/corr/abs-1910-00727}]}
\begin{center}
\begin{figure}
\begin{minipage}{0.45\linewidth}
\includegraphics[width=\linewidth]{presentation/img/adversarial_rendering_results/correct_car.png}
\includegraphics[width=\linewidth]{presentation/img/adversarial_rendering_results/correct_pedestrian.png}
\includegraphics[width=\linewidth]{img/adversarial_rendering_results/correct_car.png}
\includegraphics[width=\linewidth]{img/adversarial_rendering_results/correct_pedestrian.png}
\end{minipage}
\begin{minipage}{0.45\linewidth}
\includegraphics[width=\linewidth]{presentation/img/adversarial_rendering_results/incorrect_car.png}
\includegraphics[width=\linewidth]{presentation/img/adversarial_rendering_results/incorrect_pedestrian.png}
\includegraphics[width=\linewidth]{img/adversarial_rendering_results/incorrect_car.png}
\includegraphics[width=\linewidth]{img/adversarial_rendering_results/incorrect_pedestrian.png}
\end{minipage}
\centering
\caption{Left: Original images, features are correctly identified.\\