\begin{figure*}[!ht]
    \centering
    % Left subplot
    \begin{minipage}[t]{0.29\textwidth}
        \centering
        \large \makecell[t]{ \textbf{Stein Variational CMA-ES}\\ \textbf{(SV-CMA-ES)}: Multiple ES\\ Populations Optimize Fitness \\and Diversity }\\[.5em]
        \includegraphics[page=6, width=\linewidth, trim={0cm 6.5cm 33.5cm 3cm}, clip]{imgs/sves_plot.pdf}\\[0.25em]
        \includegraphics[page=6, width=.875\linewidth, trim={0cm 2cm 33.5cm 16.5cm}, clip]{imgs/sves_plot.pdf}
        % old legend margins
        % \includegraphics[page=5, width=.9\linewidth, trim={0cm 3cm 33.5cm 5cm},clip]{imgs/sves_plot-11.pdf}\\
        % \vspace{1mm} \includegraphics[page=5, width=1\linewidth, trim={0cm 0cm 33.5cm 20cm}, clip]{imgs/sves_plot-11.pdf}
    \end{minipage}\hspace{1mm}
    % Middle subplot
    \begin{minipage}[t]{0.29\textwidth}
        \centering
        \large \textbf{Quantitative Performance} \\
        Density Approximation\\[.1em]
        \hspace{-2mm} \includegraphics[width=0.65\linewidth]{imgs/meta_mmd_convergence_legend}\\
        Reinforcement Learning\\[.1em]
        \hspace{-2mm} \includegraphics[width=0.65\linewidth]{imgs/meta_rl}
    \end{minipage}\hspace{1mm}
    % Right subplot
    \begin{minipage}[t]{0.29\textwidth}
        \centering
        \large \makecell[t]{\textbf{Qualitative Performance}\\ Solution Quality \& Diversity}\\[0.5em]
        \begin{tabular}{cc}
        \includegraphics[width=0.35\linewidth]{imgs/gmm/dummy_gt}
        & 
        \includegraphics[width=0.35\linewidth]{imgs/gmm/GMMBB_SVGD_ESsamples}
        \\
        \small Ground Truth & \small SV-CMA-ES\\[0.5em]
        \includegraphics[width=0.35\linewidth]{imgs/gmm/dummy_ParallelCMA-ES}
        &
        \includegraphics[width=0.35\linewidth]{imgs/gmm/dummy_CMA_ES}\\
        \makecell{\small Multi-Pop. CMA-ES \\[-1mm] \small (kernel-free)} & \small CMA-ES\\
        \end{tabular}
    \end{minipage}

    \caption{\textbf{Left}: Illustration of Stein Variational CMA-ES. 
    Multiple ES search distributions are updated in parallel, similar to the SVGD step. \textbf{Middle}: Quantitative comparison of different methods for sampling and RL control tasks. SV-CMA-ES obtains higher quality solutions than existing gradient-free SVGD-based approaches. \textbf{Right}: Qualitative comparison of different CMA-ES-based methods unveils that SV-CMA-ES generates more diverse samples than other methods. The full experimental details can be found in \cref{secExp}.
    }
    \label{fig:sves-overview}
\end{figure*}