OmniLottie/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

## ⚠️ Dataset Disclaimer

### Intended Use
The MMLottie-2M Dataset (the "Dataset") is provided **exclusively for research and non-commercial purposes**. Any commercial use, redistribution for profit, or deployment in commercial products is strictly prohibited without explicit authorization.

### Data Source & Intellectual Property
- The Dataset is compiled from content that was originally publicly available on third-party websites.
- **All copyrights, trademarks, and other intellectual property rights in the original content remain with their respective owners.**
- The inclusion of any content in this Dataset does not imply endorsement, authorization, sponsorship, or any affiliation with the original content creators or rights holders.
- The processing, filtering, and reorganization performed by the authors do not alter the ownership or intellectual property status of the underlying content.

### No Warranties
The Dataset is provided **"AS IS" and "AS AVAILABLE"**, without warranties of any kind, either express or implied, including but not limited to:
- Accuracy, completeness, or reliability of the data
- Merchantability or fitness for a particular purpose
- Non-infringement of third-party rights
- Freedom from errors, bugs, or harmful components

### Limitation of Liability
**Under no circumstances shall the authors, contributors, or affiliated organizations be liable for any direct, indirect, incidental, special, consequential, or punitive damages** arising from or related to:
- The use or inability to use the Dataset
- Any errors or omissions in the Dataset
- Any claims by third parties regarding intellectual property infringement
- Any actions taken based on the content of the Dataset

### User Responsibilities
By using the Dataset, you agree that:
- You are solely responsible for ensuring compliance with all applicable laws, regulations, and third-party rights in your jurisdiction.
- You will not use the Dataset for any illegal, harmful, or unethical purposes.
- You will properly attribute the Dataset in any resulting publications or works.

### Content Removal Requests
If you are a rights holder and believe that any content in this Dataset infringes your intellectual property rights, please contact us immediately. We are committed to addressing legitimate concerns and will promptly remove any content upon verification of valid claims.

---

## 📧 Contact

For questions, concerns, or content removal requests, please reach out through:

- **Email**: [[e-mail]](mailto:[e-mail])
- **GitHub Issues**: [https://github.com/OpenVGLab/OmniLottie/issues](https://github.com/OpenVGLab/OmniLottie/issues)

## 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