Sssaasss/MMLottie-2M

--- license: cc-by-nc-sa-4.0 language: - en tags: - lottie - animation - vector-graphics - motion-graphics - multi-modal size_categories: - 1M<n<10M configs: - config_name: Lottie data_files: data/Lottie/*.parquet - config_name: Lottie_SVG data_files: data/Lottie_SVG/*.parquet --- # MMLottie-2M Dataset The first large-scale Lottie animation dataset for multi-modal vector animation generation, containing ~2M samples with diverse motion patterns and visual styles. ## Dataset Overview **MMLottie-2M** consists of two complementary subsets designed to support comprehensive training for Lottie animation generation: ### 1. Lottie Subset **Native Lottie animations** collected from major online platforms including LottieFiles, IconScout, Flaticon, Iconfont, and Icons8. **Data Processing:** - Removal of irrelevant elements (base64 images, non-visual layers, After Effects expressions) - Filtering of non-parameterizable layers - Spatial normalization to 512×512 canvas - Temporal normalization to 0-16 timestamp range - Center alignment with aspect ratio preservation **Purpose:** Provides authentic motion graphics with complex layer structures and real-world motion patterns. ### 2. Lottie_SVG Subset **SVG-to-Lottie converted animations** generated from the large-scale OmniSVG collection with motion augmentation. **Generation Process:** - Base: Static SVG files from MMSVG-2M dataset - Motion Transfer: 1,678 canonical motion templates extracted from native Lottie files - Motion Patterns: Translations, zooms, rotations, opacity changes, and combinations - Augmentation: Automated keyframe injection to create diverse motion dynamics **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. **Key Characteristics:** - Motion signatures encoding temporal patterns (e.g., "fade-in + upward motion + scale-down") - Semantically clustered motion templates with caption keywords - Reduces path distribution gap from 24% to <1% - Increases animated layer coverage from 0% to 16% ## Usage ### Load specific configuration ```python from datasets import load_dataset # Load native Lottie animations dataset_lottie = load_dataset("OmniLottie/MMLottie-2M", "Lottie") # Load SVG-based Lottie animations with motion augmentation dataset_svg = load_dataset("OmniLottie/MMLottie-2M", "Lottie_SVG") ``` ### Load subset of data ```python # Load first 1000 samples from Lottie_SVG dataset_subset = load_dataset("OmniLottie/MMLottie-2M", "Lottie_SVG", split="train[:1000]") # Load 10% of Lottie data dataset_10pct = load_dataset("OmniLottie/MMLottie-2M", "Lottie", split="train[:10%]") ``` ### Load all configurations ```python # Load both configurations together dataset_all = load_dataset("OmniLottie/MMLottie-2M") ``` ## Dataset Fields | Field | Type | Description | |-------|------|-------------| | `id` | string | Unique identifier (MD5 hash) | | `source` | string | Data source ("Lottie" or "Lottie_SVG") | | `lottie_json` | string | Normalized Lottie JSON (512×512, 0-16 frames) | | `image` | Image | PNG preview image | | `video` | Video | MP4 animation (h264 encoding, random light background) | | `detail` | string | Detailed caption (subjects, objects, motion, color, style) | | `desc_en` | string | English description with temporal details | | `keywords_en` | string | Keywords emphasizing geometry and motion | | `token_length` | int64 | Token length of Lottie JSON | | `motion_type` | string | Motion pattern type (Lottie_SVG only) | | `motion_caption` | string | Motion-specific caption (Lottie_SVG only) | ## Supported Tasks This dataset supports three multi-modal vector animation generation tasks: 1. **Text-to-Lottie**: Generate Lottie animations from text descriptions 2. **Image-Text-to-Lottie**: Generate animations from image + text (foreground motion focus) 3. **Video-to-Lottie**: Generate parameterized Lottie from video demonstrations ## Data Annotation Annotations are generated using Vision-Language Models (VLMs) with a coarse-to-fine strategy: 1. **Coarse**: Overall caption covering subjects, objects, motion, color, and style 2. **Fine**: Temporal details across frames with cues like "begins with" and "then" 3. **Emphasis**: Keywords highlighting geometry and motion for better text-following ## Citation If you use this dataset, please cite: ```bibtex @article{yang2026omnilottie, title={OmniLottie: Generating Vector Animations via Parameterized Lottie Tokens}, author={Yiying Yang and Wei Cheng and Sijin Chen and Honghao Fu and Xianfang Zeng and Yujun Cai and Gang Yu and Xinjun Ma}, journal={arXiv preprint arxiv:2603.02138}, year={2026} } ``` ## Acknowledgments We thank the following projects and resources for their valuable contributions: - **Data Sources**: [LottieFiles](https://lottiefiles.com), [IconScout](https://iconscout.com), [Flaticon](https://www.flaticon.com), [Iconfont](https://www.iconfont.cn), [Icons8](https://icons8.com) - **[python-lottie](https://github.com/eltiempoes/python-lottie)**: For providing excellent tools for Lottie manipulation and processing - **[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--- 许可证：CC BY-NC-SA 4.0 语言： - 英语 标签： - Lottie - 动画 - 矢量图形 - 动态图形 - 多模态 样本规模： - 1M<n<10M 配置项： - config_name: Lottie data_files: data/Lottie/*.parquet - config_name: Lottie_SVG data_files: data/Lottie_SVG/*.parquet --- # MMLottie-2M 数据集 本数据集是首个面向多模态矢量动画生成的大规模Lottie动画数据集，包含约200万条样本，涵盖丰富多样的运动模式与视觉风格。 ## 数据集概览 **MMLottie-2M**包含两个互补子集，旨在为Lottie动画生成任务提供全面的训练支持： ### 1. Lottie 子集 从LottieFiles、IconScout、Flaticon、Iconfont及Icons8等主流在线平台采集的原生Lottie动画。 **数据预处理：** - 移除无关元素（如base64图像、非可视化图层、After Effects表达式） - 过滤不可参数化的图层 - 将画布空间归一化至512×512分辨率 - 将时间轴归一化至0-16的时间戳区间 - 保持宽高比的同时进行中心对齐 **用途：** 提供具备复杂图层结构与真实世界运动模式的原生动态图形。 ### 2. Lottie_SVG 子集 从大规模OmniSVG数据集转换而来、并经过运动增强的SVG转Lottie动画。 **生成流程：** - 基础素材：源自MMSVG-2M数据集的静态SVG文件 - 运动迁移：从原生Lottie文件中提取的1678个标准运动模板 - 运动模式：包含平移、缩放、旋转、透明度变化及其组合 - 数据增强：通过自动插入关键帧生成多样化的运动动态 **用途：** 实现视觉内容与运动语义的解耦，能够更好地对齐视觉元素与动画条件，缩小路径分布差距并提升动画图层覆盖率，从而优化模型训练效果。 **核心特性：** - 运动特征编码时间模式（例如“淡入+向上移动+缩小”） - 带有描述关键词的语义聚类运动模板 - 将路径分布差距从24%缩小至<1% - 将动画图层覆盖率从0%提升至16% ## 使用方法 ### 加载指定配置 python from datasets import load_dataset # 加载原生Lottie动画 dataset_lottie = load_dataset("OmniLottie/MMLottie-2M", "Lottie") # 加载经过运动增强的SVG转Lottie动画 dataset_svg = load_dataset("OmniLottie/MMLottie-2M", "Lottie_SVG") ### 加载数据子集 python # 从Lottie_SVG子集中加载前1000条样本 dataset_subset = load_dataset("OmniLottie/MMLottie-2M", "Lottie_SVG", split="train[:1000]") # 加载10%的Lottie子集数据 dataset_10pct = load_dataset("OmniLottie/MMLottie-2M", "Lottie", split="train[:10%]") ### 加载全部配置 python # 同时加载两个配置的全部数据 dataset_all = load_dataset("OmniLottie/MMLottie-2M") ## 数据集字段 | 字段名 | 数据类型 | 描述 | |-------|----------|------| | `id` | 字符串 | 唯一标识符（MD5哈希值） | | `source` | 字符串 | 数据来源（"Lottie"或"Lottie_SVG"） | | `lottie_json` | 字符串 | 归一化后的Lottie JSON文件（512×512分辨率，0-16帧） | | `image` | 图像 | PNG格式预览图 | | `video` | 视频 | MP4格式动画（h264编码，随机浅色背景） | | `detail` | 字符串 | 详细描述文本（涵盖主体、对象、运动、色彩、风格） | | `desc_en` | 字符串 | 带有时间细节的英文描述 | | `keywords_en` | 字符串 | 强调几何形状与运动的关键词 | | `token_length` | 64位整数 | Lottie JSON的Token长度 | | `motion_type` | 字符串 | 运动模式类型（仅Lottie_SVG子集包含） | | `motion_caption` | 字符串 | 针对运动的专属描述文本（仅Lottie_SVG子集包含） | ## 支持任务 本数据集支持三类多模态矢量动画生成任务： 1. **文本转Lottie**：根据文本描述生成Lottie动画 2. **图像-文本转Lottie**：结合图像与文本生成动画（聚焦前景运动） 3. **视频转Lottie**：根据视频演示生成参数化Lottie动画 ## 数据标注 标注采用从粗到细的策略，通过视觉语言模型（Vision-Language Model, VLM）生成： 1. **粗标注**：涵盖主体、对象、运动、色彩与风格的整体描述 2. **细标注**：包含帧间时间细节的描述，使用“开始于”“随后”等提示词 3. **重点标注**：提取强调几何形状与运动的关键词，提升文本对齐效果 ## 引用方式 若您使用本数据集，请引用以下文献： bibtex @article{yang2026omnilottie, title={OmniLottie: Generating Vector Animations via Parameterized Lottie Tokens}, author={Yiying Yang and Wei Cheng and Sijin Chen and Honghao Fu and Xianfang Zeng and Yujun Cai and Gang Yu and Xinjun Ma}, journal={arXiv preprint arxiv:2603.02138}, year={2026} } ## 致谢 感谢以下项目与资源提供的宝贵支持： - **数据来源**：[LottieFiles](https://lottiefiles.com)、[IconScout](https://iconscout.com)、[Flaticon](https://www.flaticon.com)、[Iconfont](https://www.iconfont.cn)、[Icons8](https://icons8.com) - **[python-lottie](https://github.com/eltiempoes/python-lottie)**：提供用于Lottie动画处理与操作的优质工具 - **[MMSVG-Icon](https://huggingface.co/datasets/OmniSVG/MMSVG-Icon)**与**[MMSVG-Illustration](https://huggingface.co/datasets/OmniSVG/MMSVG-Illustration)**：为我们的多模态数据整理方法提供了灵感