Improve dataset card: add paper link, task categories, and correct license

README.md

@@ -1,4 +1,9 @@
 ---
+language:
+- en
+license: apache-2.0
+task_categories:
+- any-to-any
 dataset_info:
   features:
   - name: id
@@ -31,9 +36,6 @@ configs:
     path: data/real-*
   - split: synthetic
     path: data/synthetic-*
-license: cc-by-nc-sa-4.0
-language:
-- en
 tags:
 - Lottie
 - animation
@@ -41,21 +43,40 @@ tags:
 - multimodal
 ---
 
-## Dataset Description
+# MMLottieBench
+
+[**Project Page**](https://openvglab.github.io/OmniLottie/) | [**Paper**](https://huggingface.co/papers/2603.02138) | [**GitHub**](https://github.com/OpenVGLab/OmniLottie) | [**Demo**](https://huggingface.co/spaces/OmniLottie/OmniLottie)
+
+**MMLottieBench** is a comprehensive evaluation protocol for multi-modal vector animation generation, introduced in the paper [OmniLottie: Generating Vector Animations via Parameterized Lottie Tokens](https://huggingface.co/papers/2603.02138).
 
-**MMLottieBench** is a comprehensive evaluation protocol for multi-modal vector animation generation. The lack of mature and standardized benchmarks and metrics for vector animation generation poses significant challenges in evaluating (1) the quality of generated vector animations and (2) the extent to which generators faithfully follow multi-modal instructions.
+The lack of mature and standardized benchmarks and metrics for vector animation generation poses significant challenges in evaluating (1) the quality of generated vector animations and (2) the extent to which generators faithfully follow multi-modal instructions.
+
+## Dataset Description
 
 Our benchmark addresses these challenges by providing:
 - **Real Subset**: 450 samples curated from artist-designed Lottie animations
 - **Synthetic Subset**: 450 samples generated using state-of-the-art AI models
 - **Three Tasks**: Text-to-Lottie, Text-Image-to-Lottie, and Video-to-Lottie
 
-## Motivation
+## Sample Usage
 
-MMLottieBench aims to construct a benchmark that:
-1. **Reliably reflects a model's practical utility** in real-world scenarios
-2. **Avoids train-test overlap** that commonly arises in conventional dataset splits
-3. **Ensures fairness and long-term robustness** by including synthetic data to mitigate potential contamination from future models
+You can load the dataset using the `datasets` library:
+
+```python
+from datasets import load_dataset
+
+# Load the entire dataset
+dataset = load_dataset("OmniLottie/MMLottieBench")
+
+# Access specific subsets
+real_subset = dataset["real"]
+synthetic_subset = dataset["synthetic"]
+
+# Filter by task type
+text2lottie = real_subset.filter(lambda x: x["task_type"] == "Text-to-Lottie")
+image2lottie = real_subset.filter(lambda x: x["task_type"] == "Text-Image-to-Lottie")
+video2lottie = real_subset.filter(lambda x: x["task_type"] == "Video-to-Lottie")
+```
 
 ## Dataset Structure
 
@@ -76,95 +97,6 @@ MMLottieBench aims to construct a benchmark that:
 | subset | string | Subset type: "Real" or "Synthetic" |
 | url | string | Source URL for synthetic samples (None for real samples) |
 
-### Task Distribution
-
-#### Real Subset (450 samples)
-- **Text-to-Lottie**: 150 textual prompts derived from real Lottie animations
-- **Text-Image-to-Lottie**: 150 samples with one rendered frame and textual description from real Lottie animations
-- **Video-to-Lottie**: 150 rendered animation videos from real Lottie files
-
-#### Synthetic Subset (450 samples)
-- **Text-to-Lottie**: 150 textual prompts synthesized using GPT-4o
-- **Text-Image-to-Lottie**: 150 vector-style images generated by Gemini-3-Pro Image with motion descriptions
-- **Video-to-Lottie**: 150 reference videos generated by Seedance 1.0 from shape and motion descriptions
-
-## Benchmark Construction
-
-### Real Subset
-
-The **Real Subset** consists of samples curated from artist-designed Lottie animations collected from professional designers. All evaluation samples are **strictly disjoint from the training data**, ensuring assessment on genuinely unseen, real-world content.
-
-**Key Features:**
-- Professionally designed by artists
-- Diverse styles and complexity levels
-- Real-world animation patterns
-- Zero overlap with training data
-
-### Synthetic Subset
-
-To ensure the fairness and long-term robustness of our benchmark—particularly to mitigate potential contamination from future models trained on overly similar data—we construct a complementary **Synthetic Subset** via instruction-based synthesis using state-of-the-art generative models.
-
-#### Text-to-Lottie Synthesis
-
-We synthesize 150 textual prompts using **GPT-4o** with carefully designed meta-prompts. The generation instruction ensures high-quality, diverse, and challenging animation prompts suitable for evaluating Lottie generation models.
-
-**Key Requirements:**
-- **Icon-friendly design**: Flat 2D vector style with transform-only animations (position/rotation/scale/opacity)
-- **Color specification**: Every prompt explicitly specifies solid colors (e.g., "a yellow star", "a blue folder")
-- **Multi-phase motion**: At least 3 distinct motion steps with clear temporal order
-- **Simultaneous motion**: 70+ prompts include concurrent transformations (e.g., "rotating while sliding")
-- **Return-to-start loops**: Each animation must loop seamlessly back to the initial state
-
-**Motion Complexity Distribution:**
-- **Easy (30 prompts)**: Single object with 3 motion steps
-- **Medium (70 prompts)**: Single object with 4+ steps or 2 coordinated objects
-- **Hard (50 prompts)**: Single object with 5-6 steps or 2-3 coordinated objects with staggered timing
-
-**Object Type Coverage:**
-- UI Icons (50): Loading, progress, buttons, notifications, navigation, media controls
-- Common Icons (40): Communication, files, documents, tools, commerce symbols
-- Simple Illustrations (30): Nature, animals, food items (minimal icon-style)
-- Shapes & Abstract (20): Basic geometric shapes and abstract symbols
-- People & Characters (10): Simple stick figures or silhouettes
-
-**Allowed Motion Primitives:**
-- **Position**: sliding, gliding, drifting, moving, floating, dropping, rising
-- **Rotation**: rotating, spinning, turning, tilting, swinging, wobbling
-- **Scale**: growing, shrinking, pulsing, expanding, compressing, bouncing
-- **Opacity**: fading in/out, appearing, disappearing, flashing, brightening, dimming
-
-**Example Prompts:**
-- "a pink cupcake fading in, sliding upward while shrinking, then sliding downward to its start while bouncing, and finally fading out, looping smoothly"
-- "a cyan arrow loop symbol with appearing, rising while spinning, stays still briefly, then dropping back to its start while turning, repeating in a steady loop"
-
-#### Text-Image-to-Lottie Synthesis
-
-1. **Prompt Generation**: Using GPT-4o to generate diverse image descriptions suitable for vector animation
-2. **Image Generation**: Using **Gemini-3-Pro Image** to generate corresponding vector-style images
-3. **Motion Description**: Adding animation descriptions to guide the generation
-
-#### Video-to-Lottie Synthesis
-
-1. **Description Generation**: Creating shape and motion descriptions using GPT-4o
-2. **Video Generation**: Using **Seedance 1.0** to generate reference videos with parameters:
-   - Resolution: 480p
-   - Duration: 2 seconds
-   - FPS: 8
-   - Aspect Ratio: 1:1
-   - Style: Pure 2D vector Lottie animation
-
-**Why Synthetic Subset?**
-
-The synthetic nature of MMLottieBench's Synthetic Subset provides several key advantages:
-
-| Advantage | Description |
-|-----------|-------------|
-| **True Generalization Test** | Models cannot have seen these exact samples during training |
-| **Controlled Diversity** | Systematic coverage of styles, complexities, and animation patterns |
-| **Reproducibility** | The entire synthesis process is documented and released |
-| **Fairness** | No model has an unfair advantage from training data overlap |
-| **Long-term Robustness** | Reduces risk of benchmark contamination in future models |
-
 ## Evaluation Metrics
 
 MMLottieBench evaluates models across multiple dimensions to comprehensively assess both visual quality and semantic alignment:
@@ -174,9 +106,7 @@ MMLottieBench evaluates models across multiple dimensions to comprehensively ass
 - **FVD (Fréchet Video Distance) ↓**: Measures the visual quality of generated animations
 - **CLIP Similarity ↑**: Evaluates alignment between text prompts and rendered animation frames
 - **Object Alignment (Obj. Align) ↑** (0-10): Measures object presence, type, count, visual traits, and spatial relations
-- **Motion Alignment (Motion Align) ↑** (0-10): Assesses correctness of motion type, direction, magnitude, target objects, and smoothness (evaluated independently of object accuracy)
-
-Both alignment metrics use **Claude-3.5-Sonnet** as an LLM judge. Invalid generations are omitted from evaluation, and blank outputs receive a score of 0.
+- **Motion Alignment (Motion Align) ↑** (0-10): Assesses correctness of motion type, direction, magnitude, target objects, and smoothness
 
 ### Video-to-Lottie Task
 
@@ -185,133 +115,6 @@ Both alignment metrics use **Claude-3.5-Sonnet** as an LLM judge. Invalid genera
 - **SSIM (Structural Similarity Index) ↑**: Measures structural similarity with reference video
 - **DINO ↑**: Evaluates semantic similarity using self-supervised features
 
-### Model Efficiency Metrics
-
-- **Token Efficiency (# Tokens)**: Average token length of generated Lottie JSON using the Qwen2.5-VL tokenizer
-- **Computational Cost (Time)**: Average generation time per sample in seconds. For closed-source APIs, timing includes full API latency for realistic comparison
-- **Success Rate**: Percentage of valid Lottie animations successfully generated
-
-## Quantitative Evaluations
-
-We provide comprehensive quantitative comparisons between state-of-the-art baseline methods across both **Real Subset** and **Synthetic Subset**. **Bold** numbers and <u>underlined</u> numbers represent the best and second-best performance respectively.
-
-### Real Subset Results
-
-#### Text-to-Lottie Task
-
-| Methods | Time(s) | # Tokens | Success Rate | FVD↓ | CLIP↑ | Obj.↑ | Motion↑ |
-|---------|---------|----------|--------------|------|-------|-------|---------|
-| DeepSeekV3 | 43.40 | 2.3k | 9.3% | 671.80 | 0.2677 | 1.51 | 2.09 |
-| Qwen2.5-VL(3B) | 27.97 | 0.5k | 0.0% | - | - | - | - |
-| GPT-4o | 43.40 | 1.4k | 12.7% | 715.73 | 0.2600 | 0.73 | 0.71 |
-| Recraft | - | 54.1k | 77.3% | <u>300.70</u> | **0.2950** | **4.70** | <u>4.68</u> |
-| **Ours** | 33.71 | 21.2k | 88.3% | **202.14** | <u>0.2748</u> | <u>4.44</u> | **5.94** |
-
-#### Text-Image-to-Lottie Task
-
-| Methods | Time(s) | # Tokens | Success Rate | FVD↓ | CLIP↑ | Obj.↑ | Motion↑ |
-|---------|---------|----------|--------------|------|-------|-------|---------|
-| Qwen2.5-VL(3B) | 33.60 | 0.4k | 0.0% | - | - | - | - |
-| GPT-4o | 31.18 | 1.5k | 28.0% | 546.65 | 0.2557 | 1.18 | 0.95 |
-| AniClipart | 1212.34 | - | 87.3% | <u>266.46</u> | **0.2935** | <u>4.51</u> | <u>3.47</u> |
-| Livesketch | 723.23 | - | 91.3% | 868.18 | 0.2309 | 2.84 | 2.42 |
-| **Ours** | 88.57 | 23.4k | 93.3% | **180.27** | <u>0.2666</u> | **5.10** | **4.44** |
-
-#### Video-to-Lottie Task
-
-| Methods | Time(s) | # Tokens | Success Rate | FVD↓ | PSNR↑ | SSIM↑ | DINO↑ |
-|---------|---------|----------|--------------|------|-------|-------|-------|
-| Qwen2.5-VL(3B) | 49.31 | 1.0k | 0.0% | - | - | - | - |
-| GPT-4o | 45.61 | 1.1k | 9.2% | <u>639.13</u> | 13.34 | <u>0.81</u> | 0.80 |
-| Gemini3-Pro | 16.19 | 1.0k | 0.0% | 1076.22 | <u>14.54</u> | 0.79 | <u>0.88</u> |
-| **Ours** | 110.77 | 36.8k | 88.1% | **227.11** | **16.08** | **0.82** | **0.92** |
-
-### Synthetic Subset Results
-
-#### Text-to-Lottie Task
-
-| Methods | Time(s) | # Tokens | Success Rate | FVD↓ | CLIP↑ | Obj.↑ | Motion↑ |
-|---------|---------|----------|--------------|------|-------|-------|---------|
-| DeepSeekV3 | 56.71 | 2.3k | 7.4% | 483.11 | 0.2677 | 1.43 | 1.98 |
-| Qwen2.5-VL(3B) | 94.36 | 0.4k | 0.0% | - | - | - | - |
-| GPT-4o | 57.59 | 0.9k | 8.8% | 637.29 | 0.2600 | 0.45 | 0.66 |
-| Recraft | - | 50.8k | 77.3% | <u>438.97</u> | **0.2950** | <u>4.33</u> | <u>3.12</u> |
-| **Ours** | 37.93 | 13.4k | 82.1% | **206.35** | <u>0.2748</u> | **4.31** | **5.63** |
-
-#### Text-Image-to-Lottie Task
-
-| Methods | Time(s) | # Tokens | Success Rate | FVD↓ | CLIP↑ | Obj.↑ | Motion↑ |
-|---------|---------|----------|--------------|------|-------|-------|---------|
-| Qwen2.5-VL(3B) | 31.06 | 0.3k | 0.0% | - | - | - | - |
-| GPT-4o | 37.80 | 1.2k | 22.0% | 560.11 | 0.2557 | 1.02 | 0.66 |
-| AniClipart | 1123.24 | - | 88.7% | <u>308.54</u> | **0.2935** | <u>4.11</u> | <u>2.79</u> |
-| Livesketch | 742.23 | - | 91.9% | 1058.32 | 0.2309 | 2.01 | 1.91 |
-| **Ours** | 84.80 | 16.3k | 92.9% | **225.45** | <u>0.2666</u> | **4.44** | **3.98** |
-
-#### Video-to-Lottie Task
-
-| Methods | Time(s) | # Tokens | Success Rate | FVD↓ | PSNR↑ | SSIM↑ | DINO↑ |
-|---------|---------|----------|--------------|------|-------|-------|-------|
-| Qwen2.5-VL(3B) | 42.19 | 1.1k | 0.0% | - | - | - | - |
-| GPT-4o | 26.26 | 0.9k | 7.4% | 576.52 | 13.33 | 0.71 | 0.78 |
-| Gemini3-Pro | 13.77 | 1.3k | 0.0% | 1550.65 | <u>13.89</u> | <u>0.75</u> | <u>0.83</u> |
-| **Ours** | 109.53 | 41.4k | 80.7% | 342.65 | **15.76** | **0.79** | **0.88** |
-
-### Key Observations
-
-1. **Superior Visual Quality**: Our method consistently achieves the best FVD scores across all tasks and subsets, demonstrating superior visual quality in generated Lottie animations.
-
-2. **Strong Motion Alignment**: Our model excels in motion alignment, significantly outperforming baselines in capturing and reproducing complex animation patterns (5.94 vs 4.68 on Real Text-to-Lottie).
-
-3. **High Success Rate**: With success rates of 80-93%, our method reliably generates valid Lottie animations, far exceeding general-purpose VLMs like GPT-4o (7-28%) and Qwen2.5-VL (0%).
-
-4. **Balanced Performance**: While some baselines excel in specific metrics (e.g., Recraft in CLIP score), our method achieves the best overall balance across visual quality, semantic alignment, and generation reliability.
-
-5. **Token Efficiency Trade-off**: Our method uses more tokens (13-42k) compared to VLM baselines (0.3-2.3k) but significantly fewer than optimization-based methods like Recraft (50-54k), striking a balance between expressiveness and efficiency.
-
-```python
-from datasets import load_dataset
-
-# Load the entire dataset
-dataset = load_dataset("OmniLottie/MMLottieBench")
-
-# Access specific subsets
-real_subset = dataset["real"]
-synthetic_subset = dataset["synthetic"]
-
-# Filter by task type
-text2lottie = real_subset.filter(lambda x: x["task_type"] == "Text-to-Lottie")
-image2lottie = real_subset.filter(lambda x: x["task_type"] == "Text-Image-to-Lottie")
-video2lottie = real_subset.filter(lambda x: x["task_type"] == "Video-to-Lottie")
-
-# Example: iterate over text-to-lottie samples
-for sample in text2lottie:
-    print(f"ID: {sample['id']}")
-    print(f"Text: {sample['text']}")
-    print(f"Subset: {sample['subset']}")
-    # Generate Lottie animation based on the prompt
-```
-
-
-
-## Benchmark Statistics
-
-### Overall Statistics
-
-| Subset | Text-to-Lottie | Text-Image-to-Lottie | Video-to-Lottie | Total |
-|--------|----------------|----------------------|-----------------|-------|
-| Real | 150 | 150 | 150 | 450 |
-| Synthetic | 150 | 150 | 150 | 450 |
-| **Total** | **300** | **300** | **300** | **900** |
-
-### Data Characteristics
-
-- **ID Format**: MD5 hash for unique identification
-- **Text Length**: Varies from brief descriptions to detailed motion specifications
-- **Image Format**: PNG with transparency
-- **Video Format**: MP4, 480p resolution, 8 FPS
-- **Animation Patterns**: Fading, sliding, rotating, scaling, bouncing, wobbling, and combinations
-
 ## Citation
 
 If you use MMLottieBench in your research, please cite:
@@ -327,23 +130,4 @@ If you use MMLottieBench in your research, please cite:
 
 ## License
 
-This dataset is released under the Apache 2.0 License.
-
-## Acknowledgments
-
-- Real Subset samples are curated from professional Lottie designers
-- Synthetic Subset is generated using GPT-4o, Gemini-3-Pro Image, and Seedance 1.0
-- Special thanks to the open-source community for tools and frameworks
-
-## Contact
-
-For questions, issues, or contributions, please open an issue on our [GitHub repository](https://github.com/OpenVGLab/OmniLottie) or contact us at [25113050158@m.fudan.edu.cn].
-
-## Updates
-
-- **2026-02**: Initial release with 900 samples across 6 task-subset combinations
-- **2026-02**: Added comprehensive documentation and evaluation metrics
-
----
-
-**Note**: This benchmark is designed for research purposes to advance the field of vector animation generation. All synthetic data generation processes are fully documented to ensure transparency and reproducibility.
+This dataset is released under the **Apache 2.0 License**.