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@@ -46,3 +46,5 @@ static/videos/shiba.mp4 filter=lfs diff=lfs merge=lfs -text
 static/videos/steve.mp4 filter=lfs diff=lfs merge=lfs -text
 static/videos/teaser.mp4 filter=lfs diff=lfs merge=lfs -text
 static/videos/toby.mp4 filter=lfs diff=lfs merge=lfs -text

 static/videos/steve.mp4 filter=lfs diff=lfs merge=lfs -text
 static/videos/teaser.mp4 filter=lfs diff=lfs merge=lfs -text
 static/videos/toby.mp4 filter=lfs diff=lfs merge=lfs -text
+static/images/*.png filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

index.html CHANGED Viewed

@@ -3,10 +3,10 @@
 <head>
   <meta charset="utf-8">
   <meta name="description"
-        content="Deformable Neural Radiance Fields creates free-viewpoint portraits (nerfies) from casually captured videos.">
-  <meta name="keywords" content="Nerfies, D-NeRF, NeRF">
   <meta name="viewport" content="width=device-width, initial-scale=1">
-  <title>Nerfies: Deformable Neural Radiance Fields</title>
   <link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro"
         rel="stylesheet">
@@ -15,16 +15,55 @@
   <link rel="stylesheet" href="./static/css/bulma-carousel.min.css">
   <link rel="stylesheet" href="./static/css/bulma-slider.min.css">
   <link rel="stylesheet" href="./static/css/fontawesome.all.min.css">
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   <link rel="icon" href="./static/images/favicon.svg">
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   <script src="./static/js/bulma-carousel.min.js"></script>
   <script src="./static/js/bulma-slider.min.js"></script>
   <script src="./static/js/index.js"></script>
 </head>
 <body>
@@ -33,38 +72,45 @@
     <div class="container is-max-desktop">
       <div class="columns is-centered">
         <div class="column has-text-centered">
-          <h1 class="title is-1 publication-title">Nerfies: Deformable Neural Radiance Fields</h1>
           <div class="is-size-5 publication-authors">
             <span class="author-block">
-              <a href="https://keunhong.com" target="_blank">Keunhong Park</a><sup>1</sup>,</span>
             <span class="author-block">
-              <a href="https://utkarshsinha.com" target="_blank">Utkarsh Sinha</a><sup>2</sup>,</span>
             <span class="author-block">
-              <a href="https://jonbarron.info" target="_blank">Jonathan T. Barron</a><sup>2</sup>,
             </span>
             <span class="author-block">
-              <a href="http://sofienbouaziz.com" target="_blank">Sofien Bouaziz</a><sup>2</sup>,
             </span>
             <span class="author-block">
-              <a href="https://www.danbgoldman.com" target="_blank">Dan B Goldman</a><sup>2</sup>,
             </span>
             <span class="author-block">
-              <a href="https://homes.cs.washington.edu/~seitz/" target="_blank">Steven M. Seitz</a><sup>1,2</sup>,
             </span>
             <span class="author-block">
-              <a href="http://www.ricardomartinbrualla.com" target="_blank">Ricardo Martin-Brualla</a><sup>2</sup>
             </span>
           </div>
           <div class="is-size-5 publication-authors">
-            <span class="author-block"><sup>1</sup>University of Washington,</span>
-            <span class="author-block"><sup>2</sup>Google Research</span>
           </div>
           <div class="column has-text-centered">
             <div class="publication-links">
               <!-- PDF Link. -->
-              <span class="link-block">
                 <a href="https://arxiv.org/pdf/2011.12948" target="_blank"
                    class="external-link button is-normal is-rounded is-dark">
                   <span class="icon">
@@ -72,9 +118,9 @@
                   </span>
                   <span>Paper</span>
                 </a>
-              </span>
               <span class="link-block">
-                <a href="https://arxiv.org/abs/2011.12948" target="_blank"
                    class="external-link button is-normal is-rounded is-dark">
                   <span class="icon">
                       <i class="ai ai-arxiv"></i>
@@ -82,7 +128,7 @@
                   <span>arXiv</span>
                 </a>
               </span>
-              <!-- Video Link. -->
               <span class="link-block">
                 <a href="https://www.youtube.com/watch?v=MrKrnHhk8IA" target="_blank"
                    class="external-link button is-normal is-rounded is-dark">
@@ -91,10 +137,10 @@
                   </span>
                   <span>Video</span>
                 </a>
-              </span>
               <!-- Code Link. -->
               <span class="link-block">
-                <a href="https://github.com/google/nerfies" target="_blank"
                    class="external-link button is-normal is-rounded is-dark">
                   <span class="icon">
                       <i class="fab fa-github"></i>
@@ -104,7 +150,7 @@
               </span>
               <!-- Dataset Link. -->
               <span class="link-block">
-                <a href="https://github.com/google/nerfies/releases/tag/0.1" target="_blank"
                    class="external-link button is-normal is-rounded is-dark">
                   <span class="icon">
                       <i class="far fa-images"></i>
@@ -120,80 +166,6 @@
   </div>
 </section>
-<section class="hero teaser">
-  <div class="container is-max-desktop">
-    <div class="hero-body">
-      <video id="teaser" autoplay muted loop playsinline height="100%">
-        <source src="./static/videos/teaser.mp4"
-                type="video/mp4">
-      </video>
-      <h2 class="subtitle has-text-centered">
-        <span class="dnerf">Nerfies</span> turns selfie videos from your phone into
-        free-viewpoint
-        portraits.
-      </h2>
-    </div>
-  </div>
-</section>
-<section class="hero is-light is-small">
-  <div class="hero-body">
-    <div class="container">
-      <div id="results-carousel" class="carousel results-carousel">
-        <div class="item item-steve">
-          <video poster="" id="steve" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/steve.mp4"
-                    type="video/mp4">
-          </video>
-        </div>
-        <div class="item item-chair-tp">
-          <video poster="" id="chair-tp" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/chair-tp.mp4"
-                    type="video/mp4">
-          </video>
-        </div>
-        <div class="item item-shiba">
-          <video poster="" id="shiba" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/shiba.mp4"
-                    type="video/mp4">
-          </video>
-        </div>
-        <div class="item item-fullbody">
-          <video poster="" id="fullbody" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/fullbody.mp4"
-                    type="video/mp4">
-          </video>
-        </div>
-        <div class="item item-blueshirt">
-          <video poster="" id="blueshirt" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/blueshirt.mp4"
-                    type="video/mp4">
-          </video>
-        </div>
-        <div class="item item-mask">
-          <video poster="" id="mask" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/mask.mp4"
-                    type="video/mp4">
-          </video>
-        </div>
-        <div class="item item-coffee">
-          <video poster="" id="coffee" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/coffee.mp4"
-                    type="video/mp4">
-          </video>
-        </div>
-        <div class="item item-toby">
-          <video poster="" id="toby" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/toby2.mp4"
-                    type="video/mp4">
-          </video>
-        </div>
-      </div>
-    </div>
-  </div>
-</section>
 <section class="section">
   <div class="container is-max-desktop">
@@ -203,233 +175,155 @@
         <h2 class="title is-3">Abstract</h2>
         <div class="content has-text-justified">
           <p>
-            We present the first method capable of photorealistically reconstructing a non-rigidly
-            deforming scene using photos/videos captured casually from mobile phones.
-          </p>
-          <p>
-            Our approach augments neural radiance fields
-            (NeRF) by optimizing an
-            additional continuous volumetric deformation field that warps each observed point into a
-            canonical 5D NeRF.
-            We observe that these NeRF-like deformation fields are prone to local minima, and
-            propose a coarse-to-fine optimization method for coordinate-based models that allows for
-            more robust optimization.
-            By adapting principles from geometry processing and physical simulation to NeRF-like
-            models, we propose an elastic regularization of the deformation field that further
-            improves robustness.
-          </p>
-          <p>
-            We show that <span class="dnerf">Nerfies</span> can turn casually captured selfie
-            photos/videos into deformable NeRF
-            models that allow for photorealistic renderings of the subject from arbitrary
-            viewpoints, which we dub <i>"nerfies"</i>. We evaluate our method by collecting data
-            using a
-            rig with two mobile phones that take time-synchronized photos, yielding train/validation
-            images of the same pose at different viewpoints. We show that our method faithfully
-            reconstructs non-rigidly deforming scenes and reproduces unseen views with high
-            fidelity.
           </p>
         </div>
       </div>
     </div>
     <!--/ Abstract. -->
-    <!-- Paper video. -->
-    <div class="columns is-centered has-text-centered">
-      <div class="column is-four-fifths">
-        <h2 class="title is-3">Video</h2>
-        <div class="publication-video">
-          <iframe src="https://www.youtube.com/embed/MrKrnHhk8IA?rel=0&amp;showinfo=0"
-                  frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
-        </div>
-      </div>
-    </div>
-    <!--/ Paper video. -->
   </div>
 </section>
 <section class="section">
   <div class="container is-max-desktop">
     <div class="columns is-centered">
-      <!-- Visual Effects. -->
       <div class="column">
-        <div class="content">
-          <h2 class="title is-3">Visual Effects</h2>
           <p>
-            Using <i>nerfies</i> you can create fun visual effects. This Dolly zoom effect
-            would be impossible without nerfies since it would require going through a wall.
           </p>
-          <video id="dollyzoom" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/dollyzoom-stacked.mp4"
-                    type="video/mp4">
-          </video>
         </div>
-      </div>
-      <!--/ Visual Effects. -->
-      <!-- Matting. -->
-      <div class="column">
-        <h2 class="title is-3">Matting</h2>
-        <div class="columns is-centered">
-          <div class="column content">
-            <p>
-              As a byproduct of our method, we can also solve the matting problem by ignoring
-              samples that fall outside of a bounding box during rendering.
-            </p>
-            <video id="matting-video" controls playsinline height="100%">
-              <source src="./static/videos/matting.mp4"
-                      type="video/mp4">
-            </video>
           </div>
         </div>
       </div>
     </div>
-    <!--/ Matting. -->
-    <!-- Animation. -->
-    <div class="columns is-centered">
-      <div class="column is-full-width">
-        <h2 class="title is-3">Animation</h2>
-        <!-- Interpolating. -->
-        <h3 class="title is-4">Interpolating states</h3>
         <div class="content has-text-justified">
           <p>
-            We can also animate the scene by interpolating the deformation latent codes of two input
-            frames. Use the slider here to linearly interpolate between the left frame and the right
-            frame.
-          </p>
-        </div>
-        <div class="columns is-vcentered interpolation-panel">
-          <div class="column is-3 has-text-centered">
-            <img src="./static/images/interpolate_start.jpg"
-                 class="interpolation-image"
-                 alt="Interpolate start reference image."/>
-            <p>Start Frame</p>
-          </div>
-          <div class="column interpolation-video-column">
-            <div id="interpolation-image-wrapper">
-              Loading...
-            </div>
-            <input class="slider is-fullwidth is-large is-info"
-                   id="interpolation-slider"
-                   step="1" min="0" max="100" value="0" type="range">
           </div>
-          <div class="column is-3 has-text-centered">
-            <img src="./static/images/interpolate_end.jpg"
-                 class="interpolation-image"
-                 alt="Interpolation end reference image."/>
-            <p class="is-bold">End Frame</p>
-          </div>
-        </div>
-        <br/>
-        <!--/ Interpolating. -->
-        <!-- Re-rendering. -->
-        <h3 class="title is-4">Re-rendering the input video</h3>
-        <div class="content has-text-justified">
-          <p>
-            Using <span class="dnerf">Nerfies</span>, you can re-render a video from a novel
-            viewpoint such as a stabilized camera by playing back the training deformations.
           </p>
         </div>
-        <div class="content has-text-centered">
-          <video id="replay-video"
-                 controls
-                 muted
-                 preload
-                 playsinline
-                 width="75%">
-            <source src="./static/videos/replay.mp4"
-                    type="video/mp4">
-          </video>
-        </div>
-        <!--/ Re-rendering. -->
-      </div>
-    </div>
-    <!--/ Animation. -->
-    <!-- Concurrent Work. -->
-    <div class="columns is-centered">
-      <div class="column is-full-width">
-        <h2 class="title is-3">Related Links</h2>
         <div class="content has-text-justified">
           <p>
-            There's a lot of excellent work that was introduced around the same time as ours.
-          </p>
-          <p>
-            <a href="https://arxiv.org/abs/2104.09125" target="_blank">Progressive Encoding for Neural Optimization</a> introduces an idea similar to our windowed position encoding for coarse-to-fine optimization.
-          </p>
-          <p>
-            <a href="https://www.albertpumarola.com/research/D-NeRF/index.html" target="_blank">D-NeRF</a> and <a href="https://gvv.mpi-inf.mpg.de/projects/nonrigid_nerf/" target="_blank">NR-NeRF</a>
-            both use deformation fields to model non-rigid scenes.
-          </p>
-          <p>
-            Some works model videos with a NeRF by directly modulating the density, such as <a href="https://video-nerf.github.io/" target="_blank">Video-NeRF</a>, <a href="https://www.cs.cornell.edu/~zl548/NSFF/" target="_blank">NSFF</a>, and <a href="https://neural-3d-video.github.io/" target="_blank">DyNeRF</a>
-          </p>
-          <p>
-            There are probably many more by the time you are reading this. Check out <a href="https://dellaert.github.io/NeRF/" target="_blank">Frank Dellart's survey on recent NeRF papers</a>, and <a href="https://github.com/yenchenlin/awesome-NeRF" target="_blank">Yen-Chen Lin's curated list of NeRF papers</a>.
           </p>
         </div>
-      </div>
-    </div>
-    <!--/ Concurrent Work. -->
   </div>
 </section>
-<section class="section" id="BibTeX">
   <div class="container is-max-desktop content">
     <h2 class="title">BibTeX</h2>
     <pre><code>@article{park2021nerfies,
   author    = {Park, Keunhong and Sinha, Utkarsh and Barron, Jonathan T. and Bouaziz, Sofien and Goldman, Dan B and Seitz, Steven M. and Martin-Brualla, Ricardo},
   title     = {Nerfies: Deformable Neural Radiance Fields},
-  journal   = {ICCV},
-  year      = {2021},
 }</code></pre>
   </div>
-</section>
-<footer class="footer">
-  <div class="container">
-    <div class="content has-text-centered">
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-      </a>
-      <a class="icon-link" href="https://github.com/keunhong" target="_blank" class="external-link" disabled>
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-            This website is licensed under a <a rel="license" target="_blank"
-                                                href="http://creativecommons.org/licenses/by-sa/4.0/">Creative
-            Commons Attribution-ShareAlike 4.0 International License</a>.
-          </p>
-          <p>
-            This means you are free to borrow the <a target="_blank"
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+        content="PRINCIPLES: Synthetic Strategy Memory for Proactive Dialogue Agents">
+  <meta name="keywords" content="Conversational Agents, Proactive Dialogue">
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+  <title>PRINCIPLES: Synthetic Strategy Memory for Proactive Dialogue Agents</title>
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+          <div style="display: flex; align-items: center; justify-content: center; margin-bottom: 20px;">
+            <h1 class="title is-1 publication-title" style="margin: 0 0 0 20px;"><img src="./static/images/principles.png" style="height: 60px; vertical-align: middle; margin-right: 5px; margin-bottom: 10px;">PRINCIPLES: Synthetic Strategy Memory for Proactive Dialogue Agents</h1>
+          </div>
           <div class="is-size-5 publication-authors">
             <span class="author-block">
+              <a href="#">Namyoung Kim,</a>
+            </span>
             <span class="author-block">
+              <a href="#">Kai Tzu-iunn Ong,</a>
+            </span>
             <span class="author-block">
+              <a href="#">Yeonjun Hwang</a>
             </span>
             <span class="author-block">
+              <a href="#">Minseok Kang</a>
             </span>
+            <br>
             <span class="author-block">
+              <a href="#">Iiseo Jihn,</a>
             </span>
             <span class="author-block">
+              <a href="#">Gayoung Kim,</a>
             </span>
             <span class="author-block">
+              <a href="#">Minju Kim,</a>
+            </span>
+            <span class="author-block">
+              <a href="#">Jinyoung Yeo</a>
             </span>
           </div>
           <div class="is-size-5 publication-authors">
+            <span class="author-block">Department of Artificial Intelligence, Yonsei University</span>
           </div>
           <div class="column has-text-centered">
             <div class="publication-links">
               <!-- PDF Link. -->
+              <!-- <span class="link-block">
                 <a href="https://arxiv.org/pdf/2011.12948" target="_blank"
                    class="external-link button is-normal is-rounded is-dark">
                   <span class="icon">
                   </span>
                   <span>Paper</span>
                 </a>
+              </span> -->
               <span class="link-block">
+                <a href="https://arxiv.org/abs/2509.17459" target="_blank"
                    class="external-link button is-normal is-rounded is-dark">
                   <span class="icon">
                       <i class="ai ai-arxiv"></i>
                   <span>arXiv</span>
                 </a>
               </span>
+              <!-- Video Link.
               <span class="link-block">
                 <a href="https://www.youtube.com/watch?v=MrKrnHhk8IA" target="_blank"
                    class="external-link button is-normal is-rounded is-dark">
                   </span>
                   <span>Video</span>
                 </a>
+              </span> -->
               <!-- Code Link. -->
               <span class="link-block">
+                <a href="https://github.com/kimnamssya/Principles" target="_blank"
                    class="external-link button is-normal is-rounded is-dark">
                   <span class="icon">
                       <i class="fab fa-github"></i>
               </span>
               <!-- Dataset Link. -->
               <span class="link-block">
+                <a href="https://huggingface.co/datasets/LangAGI-Lab/P4GPlus" target="_blank"
                    class="external-link button is-normal is-rounded is-dark">
                   <span class="icon">
                       <i class="far fa-images"></i>
   </div>
 </section>
 <section class="section">
   <div class="container is-max-desktop">
         <h2 class="title is-3">Abstract</h2>
         <div class="content has-text-justified">
           <p>
+            Dialogue agents based on large language models (LLMs) have shown promising performance in proactive dialogue, which requires effective strategy planning.
+            However, existing approaches to strategy planning for proactive dialogue face several limitations:
+            limited strategy coverage, preference bias in planning, and reliance on costly additional training.
+            To address these, we propose <img src="static/images/principles.png" style="height: 1em; vertical-align: middle;"><b>PRINCIPLES</b>: a synthetic strategy memory for proactive dialogue agents.
+            PRINCIPLES is derived through offline self-play simulations and serves as reusable knowledge that guides strategy planning during inference, eliminating the need for additional training and data annotation.
+            We evaluate PRINCIPLES in both emotional support and persuasion domains, demonstrating consistent improvements over strong baselines.
+            Furthermore, PRINCIPLES maintains its robustness across extended and more diverse evaluation settings.
           </p>
         </div>
       </div>
     </div>
     <!--/ Abstract. -->
   </div>
 </section>
 <section class="section">
   <div class="container is-max-desktop">
     <div class="columns is-centered">
       <div class="column">
+        <h2 class="title is-2">Methodology</h2>
+        <div class="tabs">
+          <ul>
+            <li id="step1-tab" class="is-active"><a href="#" onclick="showStep(1); return false;">Phase I: Principles Construction</a></li>
+            <li id="step2-tab"><a href="#" onclick="showStep(2); return false;">Phase II: Principles-driven Strategy Planning</a></li>
+          </ul>
+        </div>
+        <div id="step1-content" class="content step-content">
+          <div class="content has-text-centered">
+            <img src="static/images/main_1.png" alt="Principles Construction">
+          </div>
+          <h3 class="title is-4">Step I: Success and Failure Detection</h3>
+          <p>
+            At each turn \( t \), the agent and the user simulator generate their responses, and a critic model assigns a scalar reward \( r_t \).
+            We determine the <code>status</code> as either success or failure by evaluating whether the reward is higher than the previous turn:
+            \begin{equation}
+\text{status}(s_t, a_t, u_t) =
+\begin{cases}
+\text{1} & \text{if } r_t > r_{t-1} \\
+\text{0} & \text{otherwise}
+\end{cases}
+\end{equation}
+          </p>
+          <h3 class="title is-4">Step II: Strategy Revision</h3>
           <p>
+            Upon detecting a failure, the simulation invokes a revision step to refine the previously failed strategic decision.
+It then generates a revised strategy \(\sigma_t^{\prime}\) to re-simulate from the failure point, leveraging prior failed attempts at turn \(t\). Formally, the revised strategy is generated as:
+\begin{equation}
+\sigma_t^{\prime} = \texttt{LLM}_{\theta}(\rho_{r}; s_t, \mathcal{F}_t)
+\end{equation}
+where \(\rho_{r}\) is the revision prompt and \(\mathcal{F}_t\) denotes the set of previously failed trials at turn \(t\), defined as \( \mathcal{F}_t = \{ (\sigma_t^{1}, a_t^{1}, u_t^{1}), \dots, (\sigma_t^{n}, a_t^{n}, u_t^{n}) \} \)
+where \(n\) is the maximum number of failed attempts. This failure history guides the model to avoid previously ineffective strategies.
+          </p>
+          <h3 class="title is-4">Step III: Re-simulation via Backtracking</h3>
+          <p>
+            After generating a revised strategy \(\sigma_t^{\prime}\), the simulation backtracks to the original state \(s_t\) preceding the failure and re-simulates turn \(t\) using \(\sigma_t^{\prime}\). The agent generates a revised response \(a_t^{\prime}\), and the user simulator produces a new reply \(u_t^{\prime}\) based on the updated context.
+            \begin{equation}
+            a_t^{\prime} = \texttt{LLM}_{\theta}(\rho_{a}; s_t, \sigma_t^{\prime})
+            \end{equation}
+            \begin{equation}
+            u_t^{\prime} = \texttt{LLM}_{\theta}(\rho_{u}; s_t, a_t^{\prime})
+            \end{equation}
+          </p>
+          <h3 class="title is-4">Step IV: Principle Derivation</h3>
+          <p>
+            If the corrected turn is re-evaluated as successful (<code>status</code> == 1), indicating a transition from failure to success, we derive a principle \( \tilde{p_t} \) as a result of overcoming the failure:
+            \begin{equation}
+            \tilde{p_t} = \texttt{LLM}_{\theta}(\rho_{\psi}; s_{t}, \mathcal{T}_t^{*}, \mathcal{F}_{t})
+            \end{equation}
+            where \(\rho_{\psi}\) is a prompt designed to extract a principle from failure, and the successful revised interaction is denoted as \(\mathcal{T}_t^{*} = (\sigma_t^{*}, a_t^{*}, u_t^{*})\). The extracted principle is then added to the principle set \(\mathcal{P}\):
+            \begin{equation}
+            \mathcal{P} \leftarrow \mathcal{P} \cup \{ \tilde{p_t} \}
+            \end{equation}
           </p>
         </div>
+        <div id="step2-content" class="content step-content" style="display: none;">
+          <div class="content has-text-centered">
+            <img src="static/images/main_2.png" alt="Principles-driven Strategy Planning">
           </div>
+          <p>
+            To apply the extracted PRINCIPLES at inference time, we first identify candidate principles that closely match the current context. Since the <code>When</code> clause captures the core situation, we retrieve relevant top-\(k\) principles by comparing the current state \(s_t\) and the <code>When</code> clause using L2 distance between embedding vectors.
+Only the <code>When</code> component of each principle is used to compute similarity, allowing us to identify contextually analogous dialogue situations across diverse scenarios. We denote the set of top-\(k\) retrieved principles as \( \Sigma_t = \{\sigma_1, \dots, \sigma_k\} \subset \mathcal{P} \). Since even within the same domain, retrieved principles may not directly align with the dialogue context, we perform a reinterpretation step. Formally, the reinterpreted principles \(\tilde{\Sigma}_t\) are generated as:
+\begin{equation}
+\tilde{\Sigma}_t = \texttt{LLM}_{\theta}(\rho_{\nu}; s_t, \Sigma_t)
+\end{equation}
+where \(\rho_{\nu}\) is a reinterpretation prompt designed to adapt retrieved principles \(\Sigma_t\) to the current context. This aligns each principle with the context.
+          </p>
         </div>
       </div>
     </div>
+  </div>
+</section>
+<section class="section">
+  <div class="container is-max-desktop">
+    <!-- <div class="columns is-centered has-text-centered"> -->
+      <!-- <div class="column is-four-fifths"> -->
+        <h2 class="title is-2">Qualitative Example</h2>
         <div class="content has-text-justified">
           <p>
+            Qualitative example comparing AnE, PPDPP, and our approach based on PRINCIPLES. our
+            approach tended to combine logical coherence and emotional empathy (i.e., Balanced Support).
+          <div class="content has-text-centered">
+            <img src="static/images/figure_case.png" alt="Qualitative example" style="width: 100%;">
           </div>
           </p>
         </div>
+      <!-- </div> -->
+    <!-- </div> -->
+    <!--/ Abstract. -->
+  </div>
+</section>
+<section class="section">
+  <div class="container is-max-desktop">
+    <!-- <div class="columns is-centered has-text-centered"> -->
+      <!-- <div class="column is-four-fifths"> -->
+        <h2 class="title is-2">Main Results</h2>
         <div class="content has-text-justified">
           <p>
+            We investigate our method’s effectiveness in addressing three key challenges in strategy planning: coverage, bias, and training. For more details, please refer to our paper.
+          <div class="content has-text-centered">
+            <img src="static/images/main_results.png" alt="Main results" style="width: 100%;">
+          </div>
           </p>
         </div>
+      <!-- </div> -->
+    <!-- </div> -->
+    <!--/ Abstract. -->
   </div>
 </section>
+<!-- <section class="section" id="BibTeX">
   <div class="container is-max-desktop content">
     <h2 class="title">BibTeX</h2>
     <pre><code>@article{park2021nerfies,
   author    = {Park, Keunhong and Sinha, Utkarsh and Barron, Jonathan T. and Bouaziz, Sofien and Goldman, Dan B and Seitz, Steven M. and Martin-Brualla, Ricardo},
   title     = {Nerfies: Deformable Neural Radiance Fields},
+  journal   = {<EMNLP>},
+  year      = {2025},
 }</code></pre>
   </div>
+</section> -->

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