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Yiying Yang
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<!-- Comparison Results -->
<div class="columns is-centered" style="margin-top: 20px">
<div class="column is-full-width">
<h2 class="title is-4">Comparison with SOTA Methods</h2>
<h3 class="title is-5">Text-to-Lottie</h3>
<div class="content has-text-justified">
<p>OmniLottie achieves near-perfect success rates, the best FVD, and the strongest motion alignment compared to baselines including DeepSeek, GPT-4o, and Recraft.</p>
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<div class="columns is-centered">
<div class="column" style="align-self: center">
<img src="./assets/OmniLottie-t2lottie.png" alt="Text-to-Lottie Comparison">
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</div>
<h3 class="title is-5">Text-Image-to-Lottie</h3>
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<p>OmniLottie ranks first in FVD, object alignment, and motion alignment while maintaining high reliability. Methods such as AniClipart and LiveSketch exhibit low success rates and significantly longer runtimes.</p>
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<div class="columns is-centered">
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<img src="./assets/OmniLottie-ti2lottie.png" alt="Text-Image-to-Lottie Comparison">
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<h3 class="title is-5">Video-to-Lottie</h3>
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<p>OmniLottie preserves temporal and structural fidelity most effectively, achieving the best FVD, PSNR, SSIM, and DINO scores.</p>
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<div class="columns is-centered">
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<img src="./assets/OmniLottie-v2lottie.png" alt="Video-to-Lottie Comparison">
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<!-- Quantitative Results Table -->
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<h2 class="title is-4">Quantitative Results</h2>
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OmniLottie achieves 97.3%, 92.0%, and 90.7% success rates for Text-to-Lottie, Text-Image-to-Lottie, and Video-to-Lottie tasks respectively,
substantially outperforming all baselines while producing richer token sequences that enable more expressive and detailed vector animation generation.
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<!-- Dataset Section --> <!-- Dataset Section -->
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