README.md

metadata

title: ACMDM Motion Generation
emoji: 🎭
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.0.0
app_file: app.py
pinned: false
license: mit
hardware: gpu-t4-small

ACMDM Motion Generation

Generate human motion animations from text descriptions using the ACMDM (Absolute Coordinates Make Motion Generation Easy) model.

🎯 Features

• Text-to-Motion Generation: Create realistic human motion from natural language descriptions
• Batch Processing: Generate multiple motions at once
• Auto-Length Estimation: AI automatically determines optimal motion length
• Flexible Parameters: Adjust CFG scale, motion length, and more
• Real-time Preview: See your generated motions instantly

🚀 Usage

1. Enter a text description of the motion you want (e.g., "A person is running on a treadmill.")
2. Adjust parameters (optional):
   • Motion length (40-196 frames)
   • CFG scale (controls text alignment)
   • Auto-length estimation
3. Click "Generate Motion"
4. View and download your generated motion video

📝 Example Prompts

• "A person is running on a treadmill."
• "Someone is doing jumping jacks."
• "A person walks forward and then turns around."
• "A person is dancing energetically."

⚙️ Parameters

• Motion Length: Number of frames (40-196). Automatically rounded to multiples of 4.
• CFG Scale: Classifier-free guidance scale (1.0-10.0). Higher = more text-aligned, Lower = more diverse.
• Auto-length: Let AI estimate the optimal motion length based on your text.

🔧 Technical Details

This space uses pre-trained ACMDM models:

• Autoencoder: AE_2D_Causal
• Diffusion Model: ACMDM_Flow_S_PatchSize22
• Dataset: HumanML3D (t2m)

📚 Paper

Absolute Coordinates Make Motion Generation Easy

🤝 Citation

@article{meng2025absolute,
    title={Absolute Coordinates Make Motion Generation Easy},
    author={Meng, Zichong and Han, Zeyu and Peng, Xiaogang and Xie, Yiming and Jiang, Huaizu},
    journal={arXiv preprint arXiv:2505.19377},
    year={2025}
}

⚠️ Notes

• First generation may take 30-60 seconds (model loading)
• Subsequent generations are faster (5-15 seconds)
• GPU recommended for best performance
• Works on CPU but slower

🔗 Links

• GitHub Repository
• Project Page
• Paper