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@@ -167,42 +167,66 @@ <h1 class="title is-1 publication-title">MinD-3D: Reconstruct High-quality 3D ob
 </section>
 
 
-<!-- <section class="hero teaser">
+<section class="section">
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-      <center>
-        <img src="./imgs/1_final.gif" width="90%"/>
-        <img src="./imgs/7_final.gif" width="90%"/>
-        <img src="./imgs/209_final.gif" width="90%"/>
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+    <div class="columns is-centered has-text-centered">
+      <div class="column is-four-fifths">
+        <h2 class="title is-3">Abstract</h2>
+        <div class="content has-text-justified">
+          <p>
+            In this paper, we introduce <b>Recon3DMind</b>, an innovative task aimed at reconstructing 3D visuals from Functional Magnetic Resonance Imaging (fMRI) signals, marking a significant advancement in the fields of cognitive neuroscience and computer vision. 
+            To support this pioneering task, we present the <b>fMRI-Shape</b> dataset, which includes data from 14 participants and features 360-degree videos of 3D objects to enable comprehensive fMRI signal capture across various settings, 
+            thereby laying a foundation for future research. Furthermore, we propose <b>MinD-3D</b>, a novel and effective three-stage framework specifically designed to decode the brain's 3D visual information from fMRI signals, 
+            demonstrating the feasibility of this challenging task. The framework begins by extracting and aggregating features from fMRI frames through a neuro-fusion encoder, subsequently employs a feature bridge diffusion model to generate visual features, 
+            and ultimately recovers the 3D object via a generative transformer decoder. 
+            We assess the performance of MinD-3D using a suite of semantic and structural metrics and analyze the correlation between the features extracted by our model and the visual regions of interest (ROIs) in fMRI signals. 
+            Our findings indicate that MinD-3D not only reconstructs 3D objects with high semantic relevance and spatial similarity but also significantly enhances our understanding of the human brain's capabilities in processing 3D visual information. 
+          </p>
+        </div>
+      </div>
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-</section> -->
+</section>
 
 <section class="section">
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-        <h2 class="title is-3">Abstract</h2>
+        <h2 class="title is-3">fMRI-Shape</h2>
+        <div class="content has-text-justified">
+          <center>
+            <img src="./imgs/fmri_shape.png" width="100%"/>
+          </center>
+          <p>
+            Here are some examples of the fMRI-Shape dataset across different subjects. You can download the dataset by this link: https://huggingface.co/datasets/Fudan-fMRI/fMRI-Shape.
+          </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-3">Reconstruction</h2>
         <div class="content has-text-justified">
+          <center>
+            <img src="./imgs/visualization.png" width="100%"/>
+          </center>
           <p>
-            In this paper, we introduce <b>Recon3DMind</b>, a groundbreaking task focused on reconstructing 3D visuals from Functional Magnetic Resonance Imaging (fMRI) signals. 
-            This represents a major step forward in cognitive neuroscience and computer vision. 
-            To support this task, we present the <b>fMRI-Shape</b> dataset, utilizing 360-degree view videos of 3D objects for comprehensive fMRI signal capture. 
-            Containing 55 categories of common objects from daily life, this dataset will bolster future research endeavors. 
-            We also propose <b>MinD-3D</b>, a novel and effective three-stage framework that decodes and reconstructs the brain's 3D visual information from fMRI signals. 
-            This method starts by extracting and aggregating features from fMRI frames using a neuro-fusion encoder, 
-            then employs a feature bridge diffusion model to generate corresponding visual features, 
-            and ultimately recovers the 3D object through a generative transformer decoder. 
-            Our experiments demonstrate that this method effectively extracts features that are valid and highly correlated with visual regions of interest (ROIs) in fMRI signals. 
-            Notably, it not only reconstructs 3D objects with high semantic relevance and spatial similarity but also significantly deepens our understanding of the human brain's 3D visual processing capabilities.
+            The qualitative results generated by LEA-3D, fMRI-PTE-3D, and our method
+            are presented. GT indicates the ground-truth 3D objects. All the objects have been
+            rendered into a 2D format
           </p>
         </div>
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   </div>
 </section>
 
+
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@@ -225,6 +249,23 @@ <h2 class="title is-3">Framework</h2>
 
 
 
+
+<section class="section" id="BibTeX">
+  <div class="container is-max-desktop content">
+    <h2 class="title">BibTeX</h2>
+    <pre><code>
+@misc{gao2023mind3d,
+  title={MinD-3D: Reconstruct High-quality 3D objects in Human Brain}, 
+  author={Jianxiong Gao and Yuqian Fu and Yun Wang and Xuelin Qian and Jianfeng Feng and Yanwei Fu},
+  year={2023},
+  eprint={2312.07485},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV}
+}
+    </code></pre>
+  </div>
+</section>
+
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