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@@ -206,9 +206,9 @@ <h2 class="title is-3">Overall Framework</h2>
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-            <strong>Overall framework of DiET-GS.</strong> Stage 1 (DiET-GS) optimizes the deblurring 3DGS with %jointly leveraging the event streams and diffusion prior. 
+            <strong>Overall framework of DiET-GS.</strong> Stage 1 (<strong>DiET-GS</strong>) optimizes the deblurring 3DGS with leveraging the event streams and diffusion prior. 
             To preserve accurate color and clean details, we exploit EDI prior in multiple ways, including color supervision $C$, guidance for fine-grained details $I$ and additional regularization  $\tilde{I}$ with EDI simulation. 
-            Stage 2 (DiET-GS++) is then employed to maximize the effect of diffusion prior with introducing extra learnable parameters $\mathbf{f}_{\mathbf{g}}$. 
+            Stage 2 (<strong>DiET-GS++</strong>) is then employed to maximize the effect of diffusion prior with introducing extra learnable parameters $\mathbf{f}_{\mathbf{g}}$. 
             DiEt-GS++ further refines the rendered images from DiET-GS, effectively enhancing rich edge features. More details are explained in Sec. 4.1 and Sec. 4.2. of the main paper.
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