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Model: srk0102200/AnimTOON-3B Source: Original Platform
2026-08-15 02:23:16 +08:00
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+---
+language:
+- en
+license: mit
+library_name: transformers
+base_model: Qwen/Qwen2.5-3B-Instruct
+tags:
+- animation
+- lottie
+- svg
+- animtoon
+- vector-animation
+- text-to-animation
+- conversational
+- text-generation-inference
+datasets:
+- OmniLottie/MMLottie-2M
+pipeline_tag: text-generation
+---
+
+# AnimTOON-3B (v3): Token-Efficient Vector Animation Generation
+
+**3-4x fewer tokens than OmniLottie (CVPR 2026) for generating Lottie animations. Now with character animation support.**
+
+| | AnimTOON | OmniLottie |
+|---|---|---|
+| **Tokens (simple)** | **166** | 616 |
+| **Tokens (complex)** | **597** | 4095 |
+| **VRAM** | **5GB** | 15.2GB |
+| **FPS** | **30** | 8 |
+| **Model Size** | **3B LoRA** | 4B full |
+| **Custom Tokenizer** | **No** | Yes (40k tokens) |
+| **Accepts SVG** | **Yes** | No |
+
+## What is AnimTOON?
+
+AnimTOON is a compact, plain-text animation format that any LLM can generate. Instead of outputting 18,000+ tokens of raw Lottie JSON, AnimTOON describes animations in ~166-597 tokens of human-readable text.
+
+```
+anim fr=30 dur=120
+
+layer Logo shape
+  fill #000000
+  path sh x2
+  pos [0.5,0.5]
+  rot 0.0->-67 0.04->46 0.14->-31 0.28->0 ease=bounce
+  scale 0.0->[0,0] 0.14->[90,90] 0.28->[100,100] ease=smooth
+  opacity 0.0->0 0.14->100 ease=fade
+```
+
+This produces a complete animated .lottie file with bounce entrance, rotation wobble, and fade-in.
+
+## How to Use
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("srk0102200/AnimTOON-3B")
+model = AutoModelForCausalLM.from_pretrained(
+    "srk0102200/AnimTOON-3B",
+    dtype=torch.float16,
+    device_map="cuda"
+)
+
+prompt = "a red circle pulsing in the center with a smooth bounce"
+messages = [{"role": "user", "content": f"Generate AnimTOON animation: {prompt}"}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to("cuda")
+
+with torch.no_grad():
+    out = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
+result = tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
+print(result)
+```
+
+## Convert to .lottie
+
+```python
+# Clone: git clone https://github.com/srk0102/AnimTOON.git
+import sys; sys.path.insert(0, 'src')
+from toon_animator import animtoon_to_dotlottie_full
+
+animtoon_to_dotlottie_full(result, "output.lottie")
+# Preview at https://lottiefiles.com/preview
+```
+
+## Animate Any SVG
+
+```python
+from lottie import parsers  # pip install lottie
+
+# Convert SVG to Lottie (perfect paths)
+anim = parsers.svg.parse_svg_file("your_logo.svg")
+lottie_dict = anim.to_dict()
+
+# Generate AnimTOON animations with the model
+# Apply animations to the Lottie layers
+# Output: .lottie file with real SVG shapes + AI animations
+```
+
+See full pipeline: [test_svg_pipeline.py](https://github.com/srk0102/AnimTOON/blob/master/test_svg_pipeline.py)
+
+## Benchmark Results (Measured)
+
+**Same prompt, same hardware:**
+
+| Test | AnimTOON Tokens | OmniLottie Tokens | Ratio |
+|------|----------------|-------------------|-------|
+| Apple logo bounce | 207 (41 shape + 166 anim) | 1113 | 5.4x fewer |
+| Smiley face complex | 597 | 4095 | 6.9x fewer |
+| Simple ball bounce | 176 | 616 | 3.5x fewer |
+
+**Dataset statistics (99,650 samples):**
+- Average raw Lottie JSON: 18,202 tokens
+- Average AnimTOON: 222 tokens
+- Token reduction: 98.8%
+
+## Current Status (v3)
+
+**v3 adds character animation support** trained on Spine + DragonBones skeletal data.
+
+The model now works for:
+- Icon/logo animations (pulse, bounce, spin, fade, wobble)
+- **Character idle/walk cycles (14 layers, coordinated)**
+- **Multi-part SVG animation (47-part crab demo)**
+- Correct color matching from text descriptions
+- SVG + animation pipeline with per-part anchor points
+
+**Limitations:**
+- No shape generation (requires SVG input)
+- Model output varies between runs (temperature-dependent)
+- Position animation on shape groups not yet supported
+- Not yet trained on facial expressions
+
+## Training Details
+
+| Parameter | Value |
+|-----------|-------|
+| Base Model | Qwen/Qwen2.5-3B-Instruct |
+| Method | LoRA (r=16, alpha=32) merged into base |
+| Version | v3 (final 3B Lite release) |
+| Training Data | 99,650 (MMLottie-2M) + 10,000 (layer-aware) + 984 (Spine/DragonBones) |
+| Hardware | 1x NVIDIA RTX 5060 Ti (16GB) |
+| Framework | Unsloth |
+| Token Reduction | 98.8% vs raw Lottie JSON |
+
+## Architecture: Why Animation-Only is Better
+
+> "Asking one model to draw AND animate is like asking one person to paint AND dance at the same time."
+
+AnimTOON separates concerns:
+- **SVG provides shapes** (perfect, no hallucination, 0 tokens)
+- **Model generates animation** (focused, token-efficient)
+- **Converter merges them** (deterministic, 100% valid output)
+
+OmniLottie generates everything in one model → hallucinated shapes, token bloat (2001 tokens for a "crab" that looks like binoculars).
+
+## Links
+
+- **GitHub:** [github.com/srk0102/AnimTOON](https://github.com/srk0102/AnimTOON)
+- **PitchHut:** [pitchhut.com/project/animtoon-lottie-animation](https://www.pitchhut.com/project/animtoon-lottie-animation)
+- **OmniLottie (comparison):** [arxiv.org/abs/2603.02138](https://arxiv.org/abs/2603.02138)
+- **MMLottie-2M Dataset:** [huggingface.co/datasets/OmniLottie/MMLottie-2M](https://huggingface.co/datasets/OmniLottie/MMLottie-2M)
+
+## Citation
+
+```bibtex
+@misc{sivaramakrishna2026animtoon,
+  title={AnimTOON: Token-Efficient Vector Animation Generation via Compact Text Format},
+  author={Siva RamaKrishna},
+  year={2026},
+  url={https://github.com/srk0102/AnimTOON}
+}
+```
+
+## License
+
+MIT License - see [LICENSE](https://github.com/srk0102/AnimTOON/blob/master/LICENSE)