From e6ec1b9a25793affd94214e2f69aabeb5a9ccbf9 Mon Sep 17 00:00:00 2001 From: Yiying Yang <25113050158@m.fudan.edu.cn> Date: Fri, 27 Feb 2026 17:22:47 +0800 Subject: [PATCH] Update index.html --- index.html | 47 ----------------------------------------------- 1 file changed, 47 deletions(-) diff --git a/index.html b/index.html index 68b947a..87f2f0c 100644 --- a/index.html +++ b/index.html @@ -392,53 +392,6 @@ - -
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Comparison with SOTA Methods

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Text-to-Lottie

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OmniLottie achieves near-perfect success rates, the best FVD, and the strongest motion alignment compared to baselines including DeepSeek, GPT-4o, and Recraft.

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- Text-to-Lottie Comparison -
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Text-Image-to-Lottie

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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.

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- Text-Image-to-Lottie Comparison -
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Video-to-Lottie

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OmniLottie preserves temporal and structural fidelity most effectively, achieving the best FVD, PSNR, SSIM, and DINO scores.

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- Video-to-Lottie Comparison -
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Quantitative Results

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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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