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

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

# 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

# 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:

@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, IconScout, Flaticon, Iconfont, Icons8
• python-lottie: For providing excellent tools for Lottie manipulation and processing
• MMSVG-Icon, MMSVG-Illustration: For inspiring our multi-modal data curation approach