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journey-zhuang authored Dec 4, 2024
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Expand Up @@ -521,7 +521,7 @@ <h2 class="title is-3">Qualitative Comparisons</h2>
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<strong>Qualitative Comparisons:</strong> We compare our method against other state-of-the-art (SOTA) approaches: <em>StyleGaussian</em> (<a href="#liu2024stylegaussian">Liu et al., 2024</a>), <em>IGS2GS</em> (<a href="#igs2gs">IGS2GS Reference</a>), and <em>Ref-NPR</em> (<a href="#zhang2023ref">Zhang et al., 2023</a>) on three datasets (<em>Chair</em>, <em>Hotdog</em>, and <em>Mic</em>) using three styles: <em>Cartoon</em>, <em>Sky Painting</em>, and <em>Fire</em>.
<strong>Qualitative Comparisons:</strong> We compare our method against other state-of-the-art (SOTA) approaches: <em>StyleGaussian</em> (<a href="https://kunhao-liu.github.io/StyleGaussian/">Liu et al., 2024</a>), <em>IGS2GS</em> (<a href="https://instruct-gs2gs.github.io/">IGS2GS Reference</a>), and <em>Ref-NPR</em> (<a href="https://ref-npr.github.io/">Zhang et al., 2023</a>) on three datasets (<em>Chair</em>, <em>Hotdog</em>, and <em>Mic</em>) using three styles: <em>Cartoon</em>, <em>Sky Painting</em>, and <em>Fire</em>.
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The horizontal axis represents the compared methods, while the vertical axis displays different datasets. Our method effectively preserves the original model details, including:
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