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gaussian-prediction/index.html

@@ -228,7 +228,7 @@ <h2 class="title is-3">Ours Method</h2>
                alt="PNeRFLoc architecture."/>
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-          Optimization start with the initial 3D Gaussians. We then optimize the parameters of the 3D Gaussians, motion
+          Optimization starts with the initial 3D Gaussians. We then optimize the parameters of the 3D Gaussians, motion
           feature, and deformable MLP to build a Hyper-Canonical space. Next in the second stage, we first initialize the key points in the
           Hyper-Canonical space by a K-Means algorithm. Then we learn the time-independent weights for each Gaussian and deform
           the 3D Gaussian by key points motion. We employ a GCN (Graph Convolutional Network) to learn the relationships between
@@ -249,7 +249,7 @@ <h2 class="title is-3">Hyper-NeRF Dataset Prediction Results</h2>
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     <!-- D-NeRF video. -->
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+<!--     <div class="columns is-centered has-text-centered">
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         <h2 class="title is-3">D-NeRF Dataset Prediction Results</h2>
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@@ -274,7 +274,7 @@ <h2 class="title is-3">D-NeRF Dataset Prediction Results</h2>
               </div>
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+    </div> -->
     <!-- DNeRF More Results. -->
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