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Initial commit for OmniLottie project page
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
i-yangyiying
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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="utf-8">
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<meta name="description" content="OmniLottie: Generating Vector Animations via Parameterized Lottie Tokens">
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<meta name="viewport" content="width=device-width, initial-scale=1">
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<title>OmniLottie: Generating Vector Animations via Parameterized Lottie Tokens</title>
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</style>
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</head>
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<body>
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<section class="hero">
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<div class="hero-body">
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<div class="container is-max-desktop">
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<div class="columns is-centered">
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<div class="column has-text-centered">
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<h1 class="title is-0 publication-title">
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OmniLottie: Generating Vector Animations via Parameterized Lottie Tokens <br/>
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</h1>
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<style>
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.neurips-text {
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color: #b39ddb; /* 浅紫色 - 与OmniSVG一致 */
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text-align: center;
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margin-top: 10px;
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}
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</style>
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<div class="neurips-text">
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NeurIPS 2025
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</div>
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<div class="is-size-4 publication-authors">
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<span class="author-block">
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<a href="#">Author 1</a><sup>1,2*</sup>,</span>
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<span class="author-block">
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<a href="#">Author 2</a><sup>2*</sup>,</span>
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<span class="author-block">
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<a href="#">Author 3</a><sup>1</sup>,</span>
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<span class="author-block">
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<a href="#">Author 4</a><sup>2</sup>,</span>
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<span class="author-block">
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<a href="#">Author 5</a><sup>1</sup>,</span>
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</div>
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<div class="is-size-4 publication-authors">
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<span class="author-block">
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<a href="#">Author 6</a><sup>2</sup>,</span>
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<span class="author-block">
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<a href="#">Author 7</a><sup>2</sup>,</span>
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<span class="author-block">
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<a href="#">Author 8</a><sup>2‡</sup>,</span>
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<span class="author-block">
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<a href="#">Author 9</a><sup>1‡</sup>,</span>
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<span class="author-block">
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<a href="#">Author 10</a><sup>1</sup></span>
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</div>
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<div class="is-size-5 publication-authors">
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<span class="author-block"><sup>*</sup>Equal Contribution,</span>
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<span class="author-block"><sup>‡</sup>Correspondence Authors</span>
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</div>
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<div class="is-size-5 publication-authors">
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<span class="author-block"><sup>1</sup>Fudan University,</span>
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<span class="author-block"><sup>2</sup>StepFun</span>
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</div>
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<div class="column has-text-centered">
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<div class="publication-links">
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<!-- arXiv Link -->
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<span class="link-block">
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<a class="external-link button is-normal is-rounded is-dark" href="#">
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<span class="icon">
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<i class="ai ai-arxiv"></i>
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</span>
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<span>arXiv</span>
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</a>
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</span>
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<!-- Github Link -->
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<span class="link-block">
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<a href="#" class="external-link button is-normal is-rounded is-dark">
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<span class="icon">
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<i class="fab fa-github" style="color: white;"></i>
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</span>
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<span>Code</span>
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</a>
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</span>
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<!-- Weights Link -->
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<span class="link-block">
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<a href="#" class="external-link button is-normal is-rounded is-dark">
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<span class="icon">
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<img src="https://huggingface.co/front/assets/huggingface_logo.svg" alt="Hugging Face Logo" style="width: 20px; height: 20px;">
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</span>
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<span>Weights</span>
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</a>
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</span>
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<!-- Dataset Link -->
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<span class="link-block">
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<a href="#" class="external-link button is-normal is-rounded is-dark">
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<span class="icon">
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<img src="https://huggingface.co/front/assets/huggingface_logo.svg" alt="Hugging Face Logo" style="width: 20px; height: 20px;">
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</span>
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<span>MMLottie-2M Dataset</span>
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</a>
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</span>
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<!-- Demo Link -->
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<span class="link-block">
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<a href="#" class="external-link button is-normal is-rounded is-dark">
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<span class="icon">
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<i class="fas fa-play"></i>
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</span>
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<span>Demo</span>
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</a>
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</span>
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</div>
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</div>
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</div>
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</div>
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</div>
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</div>
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</section>
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<!-- Banner Video -->
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<section class="section">
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<div class="container is-max-desktop">
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<div class="columns is-centered has-text-centered" style="margin-top: -20px">
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<video src="./assets/OmniLottie-main-demo.mp4" width="100%" autoplay loop muted playsinline></video>
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</div>
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</div>
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</section>
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<!-- Abstract -->
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<section class="section">
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<div class="container is-max-desktop">
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<div class="columns is-centered has-text-centered" style="margin-top: -80px">
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<div class="column is-four-fifths">
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<h2 class="title is-2">Abstract</h2>
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<div class="content has-text-justified">
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<p>
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<b>OmniLottie</b> is a versatile framework that generates high-quality vector animations from multi-modal instructions.
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For flexible motion and visual content control, we focus on Lottie, a light-weight JSON formatting for both shapes and animation behaviors representation.
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However, the raw Lottie JSON files contain extensive invariant structural metadata and formatting tokens, posing significant challenges for learning vector animation generation.
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Therefore, we introduce a well-designed <b>Lottie tokenizer</b> that transforms JSON files into structured sequences of commands and parameters representing shapes, animation functions and control parameters.
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Such tokenizer enables us to build OmniLottie upon pretrained vision–language models to follow multi-modal interleaved instructions and generate high-quality vector animations.
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To further advance research in vector animation generation, we curate <b>MMLottie-2M</b>, a large-scale dataset of professionally designed vector animations paired with textual and visual annotations.
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With extensive experiments, we validate that OmniLottie can produce vivid and semantically aligned vector animations that adhere closely to multi-modal human instructions.
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</p>
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</div>
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</div>
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</div>
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</div>
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</section>
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<!-- Highlights -->
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<section class="hero" id="teaser" style="margin-bottom: 50px; margin-top: 50px;">
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<div class="container is-max-desktop">
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<div class="columns is-centered" style="margin-top: 15px">
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<div class="column is-full-width">
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<h2 class="title is-4">Highlights</h2>
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<div class="columns is-centered" style="text-align: center">
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<div class="column" style="align-self: center">
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<img src="./assets/OmniLottie-teaser.jpg" alt="OmniLottie Teaser">
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</div>
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</div>
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<div class="content has-text-justified" style="padding-top: 15px">
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OmniLottie is capable of generating high-quality vector animations across a wide spectrum of complexity — from simple icons to intricate animated illustrations.
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It demonstrates remarkable versatility through multiple generation modalities, including <b>Text-to-Lottie</b>, <b>Text-Image-to-Lottie</b>, and <b>Video-to-Lottie</b>,
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making it a powerful and flexible solution for diverse creative and professional animation tasks.
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</div>
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</div>
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</div>
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<!-- Method Overview -->
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<div class="columns is-centered" style="margin-top: 15px">
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<div class="column is-full-width">
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<h2 class="title is-4">Method Overview</h2>
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<div class="columns is-centered" style="text-align: center">
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<div class="column" style="align-self: center">
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<img src="./assets/OmniLottie-pipeline.jpg" alt="OmniLottie Pipeline">
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</div>
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</div>
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<div class="content has-text-justified" style="padding-top: 15px">
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OmniLottie is built on a pre-trained vision-language model (Qwen2.5-VL) and incorporates a Lottie tokenizer.
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The model tokenizes both text and image/video inputs as prefix tokens, while the Lottie tokenizer encodes vector animation commands into a unified representation space.
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The Lottie tokenizer abstracts Lottie animations into compact sequences of animation commands and control parameters, removing redundant metadata while preserving full generative flexibility.
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</div>
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</div>
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</div>
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<!-- Lottie Tokenizer Details -->
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<div class="columns is-centered" style="margin-top: 15px">
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<div class="column is-full-width">
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<h2 class="title is-4">Lottie Tokenizer</h2>
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<div class="columns is-centered" style="text-align: center">
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<div class="column" style="align-self: center">
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<img src="./assets/OmniLottie-tokenizer.jpg" alt="Lottie Tokenizer">
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</div>
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</div>
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<div class="content has-text-justified" style="padding-top: 15px">
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Our Lottie tokenizer converts raw JSON files into compact sequences of shape, effect, and animation commands with their associated parameters.
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This representation substantially shortens sequence lengths (81% reduction compared to raw JSON), improves token efficiency, and enables OmniLottie to learn a clean distribution over animation primitives.
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The tokenizer supports five fundamental layer types: Precomposition, Solid, Null, Shape, and Text layers, each identified by a unique type parameter.
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</div>
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</div>
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</div>
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</div>
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</section>
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<!-- Generation Process -->
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<section class="section">
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<div class="container is-max-desktop" style="margin-top: 30px;">
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<h3 class="title is-4">Generation Process of OmniLottie</h3>
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<div>
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<section class="section" style="overflow:hidden">
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<div class="container">
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<div id="post_images" class="carousel">
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<div class="item-1">
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<video poster="" autoplay="" muted loop="" style="pointer-events: none;">
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<source src="./assets/OmniLottie-demo-text2lottie.mp4" type="video/mp4">
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</video>
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<p class="has-text-centered"><b>Text-to-Lottie Generation</b></p>
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</div>
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<div class="item-2">
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<video poster="" autoplay="" muted loop="" style="pointer-events: none;">
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<source src="./assets/OmniLottie-demo-image2lottie.mp4" type="video/mp4">
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</video>
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<p class="has-text-centered"><b>Text-Image-to-Lottie Generation</b></p>
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</div>
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<div class="item-3">
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<video poster="" autoplay="" muted loop="" style="pointer-events: none;">
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<source src="./assets/OmniLottie-demo-video2lottie.mp4" type="video/mp4">
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</video>
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<p class="has-text-centered"><b>Video-to-Lottie Generation</b></p>
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</div>
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</div>
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</div>
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</section>
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<script src="https://cdn.jsdelivr.net/npm/bulma-carousel@4.0.3/dist/js/bulma-carousel.min.js"></script>
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||||
<script>
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bulmaCarousel.attach('#post_images', {
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||||
slidesToScroll: 1,
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slidesToShow: 1,
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loop: true,
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});
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</script>
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</div>
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<!-- Comparison Results -->
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<div class="columns is-centered" style="margin-top: 20px">
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<div class="column is-full-width">
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<h2 class="title is-4">Comparison with SOTA Methods</h2>
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<h3 class="title is-5">Text-to-Lottie</h3>
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<div class="content has-text-justified">
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<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>
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<div class="columns is-centered">
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<div class="column" style="align-self: center">
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<img src="./assets/OmniLottie-t2lottie.jpg" alt="Text-to-Lottie Comparison">
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</div>
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</div>
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<h3 class="title is-5">Text-Image-to-Lottie</h3>
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<div class="content has-text-justified">
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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>
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||||
<div class="columns is-centered">
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||||
<div class="column" style="align-self: center">
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<img src="./assets/OmniLottie-ti2lottie.jpg" alt="Text-Image-to-Lottie Comparison">
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||||
</div>
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||||
</div>
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||||
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||||
<h3 class="title is-5">Video-to-Lottie</h3>
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||||
<div class="content has-text-justified">
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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>
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||||
<div class="columns is-centered">
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||||
<div class="column" style="align-self: center">
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<img src="./assets/OmniLottie-v2lottie.jpg" alt="Video-to-Lottie Comparison">
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</div>
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||||
</div>
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||||
</div>
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||||
</div>
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||||
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<!-- Quantitative Results Table -->
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<div class="columns is-centered" style="margin-top: 20px">
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<div class="column is-full-width">
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<h2 class="title is-4">Quantitative Results</h2>
|
||||
<div class="columns is-centered">
|
||||
<div class="column" style="align-self: center">
|
||||
<img src="./assets/OmniLottie-quantitative-table.jpg" alt="Quantitative Results">
|
||||
</div>
|
||||
</div>
|
||||
<div class="content has-text-justified" style="padding-top: 15px">
|
||||
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.
|
||||
</div>
|
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</div>
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</div>
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||||
|
||||
<!-- Dataset Section -->
|
||||
<div class="columns is-centered" style="margin-top: 20px">
|
||||
<div class="column is-full-width">
|
||||
<h3 class="title is-4">MMLottie-2M Dataset</h3>
|
||||
<div class="columns is-centered">
|
||||
<div class="column" style="align-self: center">
|
||||
<img src="./assets/MMLottie-dataset.jpg" alt="MMLottie-2M Dataset">
|
||||
</div>
|
||||
</div>
|
||||
<div class="content has-text-justified" style="padding-top: 15px">
|
||||
The MMLottie-2M dataset consists of two million Lottie animations paired with multi-modal annotations including text descriptions, keyframe images, and rendered videos.
|
||||
Data is collected from major online platforms (LottieFiles, IconScout, Flaticon, Iconfont, Icons8) and augmented with SVG-derived animations.
|
||||
The dataset undergoes comprehensive processing including Lottie cleaning, spatial-temporal normalization, video rendering, and coarse-to-fine captioning using VLMs.
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Data Pipeline -->
|
||||
<div class="columns is-centered" style="margin-top: 20px">
|
||||
<div class="column is-full-width">
|
||||
<h3 class="title is-4">Data Processing Pipeline</h3>
|
||||
<div class="columns is-centered">
|
||||
<div class="column" style="align-self: center">
|
||||
<img src="./assets/OmniLottie-data-pipeline.jpg" alt="Data Processing Pipeline">
|
||||
</div>
|
||||
</div>
|
||||
<div class="content has-text-justified" style="padding-top: 15px">
|
||||
Our data processing pipeline includes five stages: (1) Data collection from web sources and SVG conversion,
|
||||
(2) Lottie cleaning to remove non-visual and non-parameterizable layers,
|
||||
(3) Spatial-temporal normalization to 512×512 canvas and unified timeline,
|
||||
(4) Video rendering with random backgrounds, and
|
||||
(5) Multi-modal annotation using a coarse-to-fine VLM captioning strategy.
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<br>
|
||||
|
||||
<!-- Related Links -->
|
||||
<div class="columns is-centered">
|
||||
<div class="column is-full-width">
|
||||
<h2 class="title is-3">Related Links</h2>
|
||||
<div class="content has-text-justified">
|
||||
<p>
|
||||
<b>We thank the following excellent open-source works:</b>
|
||||
</p>
|
||||
<p>
|
||||
<a href="https://omnisvg.github.io/"><b>OmniSVG</b></a>: A unified framework for SVG generation that leverages pre-trained VLMs. We build upon its methodology for vector graphics generation.
|
||||
</p>
|
||||
<p>
|
||||
<a href="https://icon-shop.github.io/"><b>IconShop</b></a>: The first work leveraging LLMs to generate monochrome, icon-level SVGs. We referred to its parametric implementation.
|
||||
</p>
|
||||
<p>
|
||||
<b>Concurrent Works:</b>
|
||||
</p>
|
||||
<p>
|
||||
<a href="https://aniclipart.github.io/"><b>AniClipart</b></a>: Animates static clipart using text-to-video diffusion priors.
|
||||
</p>
|
||||
<p>
|
||||
<a href="#"><b>LiveSketch</b></a>: Generates sketch animations through optimization-based approaches.
|
||||
</p>
|
||||
<p>
|
||||
<a href="https://starvector.github.io/"><b>StarVector</b></a>: Equips LLM with an image encoder for Image-to-SVG generation.
|
||||
</p>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Acknowledgements -->
|
||||
<div class="columns is-centered">
|
||||
<div class="column is-full-width">
|
||||
<h2 class="title is-3">Acknowledgements</h2>
|
||||
<div class="content has-text-justified">
|
||||
<p>
|
||||
We thank all the contributors for dataset construction and valuable discussions.
|
||||
</p>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<!-- BibTeX -->
|
||||
<section class="section" id="BibTeX">
|
||||
<div class="container is-max-desktop content">
|
||||
<h2 class="title">BibTeX</h2>
|
||||
<pre><code>@article{omnilottie2025,
|
||||
title={OmniLottie: A Unified Scalable Vector Animation Generation Model},
|
||||
author={Author1 and Author2 and Author3 and Author4 and Author5 and Author6 and Author7 and Author8 and Author9 and Author10},
|
||||
journal={arXiv preprint arXiv:XXXX.XXXXX},
|
||||
year={2025}
|
||||
}</code></pre>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
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<div class="content">
|
||||
<p style="text-align:center">
|
||||
Source code mainly borrowed from <a href="https://keunhong.com/">Keunhong Park</a>'s <a href="https://nerfies.github.io/">Nerfies website</a>.
|
||||
</p>
|
||||
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|
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