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MMLottie-2M

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motion-graphics
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Co-authored-by: OpenVGLab <OmniLottie@users.noreply.huggingface.co>

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+ ---
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+ license: cc-by-nc-sa-4.0
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+ language:
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+ - en
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+ tags:
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+ - lottie
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+ - animation
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+ - vector-graphics
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+ - motion-graphics
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+ - multi-modal
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+ size_categories:
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+ - 1M<n<10M
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+ configs:
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+ - config_name: Lottie
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+ data_files: data/Lottie/*.parquet
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+ - config_name: Lottie_SVG
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+ data_files: data/Lottie_SVG/*.parquet
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+ ---
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+
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+ # MMLottie-2M Dataset
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+
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+ The first large-scale Lottie animation dataset for multi-modal vector animation generation, containing ~2M samples with diverse motion patterns and visual styles.
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+
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+ ## Dataset Overview
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+
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+ **MMLottie-2M** consists of two complementary subsets designed to support comprehensive training for Lottie animation generation:
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+
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+ ### 1. Lottie Subset
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+
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+ **Native Lottie animations** collected from major online platforms including LottieFiles, IconScout, Flaticon, Iconfont, and Icons8.
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+
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+ **Data Processing:**
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+ - Removal of irrelevant elements (base64 images, non-visual layers, After Effects expressions)
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+ - Filtering of non-parameterizable layers
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+ - Spatial normalization to 512×512 canvas
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+ - Temporal normalization to 0-16 timestamp range
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+ - Center alignment with aspect ratio preservation
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+
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+ **Purpose:** Provides authentic motion graphics with complex layer structures and real-world motion patterns.
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+
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+ ### 2. Lottie_SVG Subset
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+
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+ **SVG-to-Lottie converted animations** generated from the large-scale OmniSVG collection with motion augmentation.
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+
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+ **Generation Process:**
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+ - Base: Static SVG files from MMSVG-2M dataset
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+ - Motion Transfer: 1,678 canonical motion templates extracted from native Lottie files
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+ - Motion Patterns: Translations, zooms, rotations, opacity changes, and combinations
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+ - Augmentation: Automated keyframe injection to create diverse motion dynamics
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+
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+ **Purpose:** Decouples visual content from motion semantics, enabling better alignment between visual components and animation conditions. Reduces the path distribution gap and increases animated layer coverage for improved model training.
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+
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+ **Key Characteristics:**
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+ - Motion signatures encoding temporal patterns (e.g., "fade-in + upward motion + scale-down")
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+ - Semantically clustered motion templates with caption keywords
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+ - Reduces path distribution gap from 24% to <1%
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+ - Increases animated layer coverage from 0% to 16%
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+
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+
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+ ## Usage
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+
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+ ### Load specific configuration
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ # Load native Lottie animations
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+ dataset_lottie = load_dataset("OmniLottie/MMLottie-2M", "Lottie")
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+
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+ # Load SVG-based Lottie animations with motion augmentation
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+ dataset_svg = load_dataset("OmniLottie/MMLottie-2M", "Lottie_SVG")
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+ ```
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+
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+ ### Load subset of data
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+
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+ ```python
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+ # Load first 1000 samples from Lottie_SVG
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+ dataset_subset = load_dataset("OmniLottie/MMLottie-2M", "Lottie_SVG", split="train[:1000]")
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+
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+ # Load 10% of Lottie data
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+ dataset_10pct = load_dataset("OmniLottie/MMLottie-2M", "Lottie", split="train[:10%]")
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+ ```
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+
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+ ### Load all configurations
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+
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+ ```python
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+ # Load both configurations together
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+ dataset_all = load_dataset("OmniLottie/MMLottie-2M")
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+ ```
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+
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+ ## Dataset Fields
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+
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+ | Field | Type | Description |
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+ |-------|------|-------------|
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+ | `id` | string | Unique identifier (MD5 hash) |
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+ | `source` | string | Data source ("Lottie" or "Lottie_SVG") |
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+ | `lottie_json` | string | Normalized Lottie JSON (512×512, 0-16 frames) |
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+ | `image` | Image | PNG preview image |
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+ | `video` | Video | MP4 animation (h264 encoding, random light background) |
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+ | `detail` | string | Detailed caption (subjects, objects, motion, color, style) |
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+ | `desc_en` | string | English description with temporal details |
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+ | `keywords_en` | string | Keywords emphasizing geometry and motion |
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+ | `token_length` | int64 | Token length of Lottie JSON |
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+ | `motion_type` | string | Motion pattern type (Lottie_SVG only) |
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+ | `motion_caption` | string | Motion-specific caption (Lottie_SVG only) |
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+
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+ ## Supported Tasks
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+
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+ This dataset supports three multi-modal vector animation generation tasks:
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+
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+ 1. **Text-to-Lottie**: Generate Lottie animations from text descriptions
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+ 2. **Image-Text-to-Lottie**: Generate animations from image + text (foreground motion focus)
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+ 3. **Video-to-Lottie**: Generate parameterized Lottie from video demonstrations
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+
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+ ## Data Annotation
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+
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+ Annotations are generated using Vision-Language Models (VLMs) with a coarse-to-fine strategy:
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+
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+ 1. **Coarse**: Overall caption covering subjects, objects, motion, color, and style
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+ 2. **Fine**: Temporal details across frames with cues like "begins with" and "then"
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+ 3. **Emphasis**: Keywords highlighting geometry and motion for better text-following
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+
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+ ## Citation
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+
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+ If you use this dataset, please cite:
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+
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+ ```bibtex
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+ @article{yang2026omnilottie,
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+ title={OmniLottie: Generating Vector Animations via Parameterized Lottie Tokens},
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+ author={Yiying Yang and Wei Cheng and Sijin Chen and Honghao Fu and Xianfang Zeng and Yujun Cai and Gang Yu and Xinjun Ma},
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+ journal={arXiv preprint arxiv:2603.02138},
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+ year={2026}
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+ }
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+ ```
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+
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+
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+ ## Acknowledgments
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+
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+ We thank the following projects and resources for their valuable contributions:
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+
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+ - **Data Sources**: [LottieFiles](https://lottiefiles.com), [IconScout](https://iconscout.com), [Flaticon](https://www.flaticon.com), [Iconfont](https://www.iconfont.cn), [Icons8](https://icons8.com)
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+ - **[python-lottie](https://github.com/eltiempoes/python-lottie)**: For providing excellent tools for Lottie manipulation and processing
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+ - **[MMSVG-Icon](https://huggingface.co/datasets/OmniSVG/MMSVG-Icon)**, **[MMSVG-Illustration](https://huggingface.co/datasets/OmniSVG/MMSVG-Illustration)**: For inspiring our multi-modal data curation approach
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+
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