repo_data/README.md

metadata

title: Movimento
emoji: �
colorFrom: purple
colorTo: blue
sdk: gradio
sdk_version: 6.14.0
python_version: '3.12'
app_file: app.py
pinned: true
license: apache-2.0
short_description: Text-driven multi-character motion generation with Qwen LLM planning

Movimento: Multi-Character Motion Generation

Text-driven interactive motion synthesis powered by Qwen LLM planning and Kimodo diffusion models on AMD hardware.

Features

• 🎭 Multi-character orchestration: Synchronize motion for multiple characters in a single scene
• 🧠 Qwen LLM planning: Convert natural language prompts to structured motion scripts
• 💾 BONES-SEED dataset: Pre-trained motions for realistic human movement
• ⚡ Real-time visualization: Viser-based 3D motion preview with playback controls
• 🎯 Interactive constraints: Hands-on guidance for character interactions (hand pose, foot contact)
• 🔄 Smooth transitions: Automatic blending between motions with multiple transition policies (cut, overlap, hold, smooth)

Usage

1. Enter a multi-character scenario: e.g., "Two characters walk together, then one sits down while the other stands."
2. Review the motion script: Qwen parses your prompt into character segments with timing and transitions
3. Adjust parameters: Configure transition styles, FPS, and postprocessing options
4. Generate motion: Diffusion models synthesize realistic human motion in real-time
5. Visualize and download: Preview in 3D, adjust playback speed, export as BVH/FBX

Architecture

User Prompt
    ↓
Qwen LLM (7B/3B/1.5B) - Script Planning
    ↓
DeterministicLoop Scheduler - Multi-character collision detection & resolution
    ↓
CharacterKimodoPlan - Segment→Motion mapping with transition policies
    ↓
Kimodo Diffusion Models - Per-character motion generation with constraints
    ↓
Viser Viewer - 3D preview & pose refinement

Technical Stack

• LLM Planner: Qwen2.5-7B via HuggingFace Inference API
• Motion Model: Kimodo (NVIDIA Labs) - text-to-motion diffusion
• Dataset: BONES-SEED (comprehensive human motion capture)
• Scheduler: Deterministic RNG-based conflict resolution for multi-character scenes
• Infrastructure: HuggingFace Spaces with AMD GPU support

Documentation

• GitHub Repository
• Kimodo Paper & Model
• BONES-SEED Dataset

Citation

If you use Movimento in your research, please cite:

@software{movimento2026,
  title={Movimento: Multi-Character Motion Generation with LLM Planning},
  author={Ted Iro Opiyo},
  year={2026},
  url={https://huggingface.co/spaces/lablab-ai-amd-developer-hackathon/movimento}
}

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Built for the lablab.ai × AMD Developer Hackathon 2026