Infinigen MCP Server
Provides a bridge to Infinigen for procedural generation of photorealistic 3D scenes, including natural environments, indoor spaces, and individual 3D assets with support for multiple export formats (OBJ, OpenUSD) and computer vision annotations (depth maps, segmentation).
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Infinigen MCP Server generate a forest scene with pine trees and a river"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Infinigen MCP Server
A Model Context Protocol (MCP) server for Infinigen - enabling AI assistants to generate photorealistic 3D scenes procedurally.
Overview
This MCP server provides a bridge between AI assistants (like Claude) and Infinigen, a powerful procedural 3D scene generator developed by Princeton Vision & Learning Lab. Through this server, AI assistants can:
Generate photorealistic natural scenes (terrain, vegetation, weather)
Create detailed indoor environments (rooms, furniture, decorations)
Produce 3D assets with various export formats (OBJ, OpenUSD, etc.)
Generate annotated data for computer vision tasks (depth maps, segmentation, etc.)
Features
π² Nature Scene Generation: Create outdoor environments with realistic terrain and vegetation
π Indoor Scene Generation: Generate furnished interior spaces
π¨ Asset Creation: Produce individual 3D objects and elements
π Batch Processing: Generate multiple scenes with different configurations
π§ Configurable: Full control over scene parameters through Infinigen's config system
Prerequisites
Node.js >= 18.0.0
Python 3.10+ with Infinigen installed
Blender (required by Infinigen)
Installation
# Clone the repository
git clone <repository-url>
cd infinigen-mcp
# Install dependencies
npm install
# Build the project
npm run buildConfiguration
Ensure Infinigen is properly installed and accessible in your Python environment. See Infinigen's installation guide for details.
Usage
Running the Server
npm startConnecting with Claude Desktop
Add to your Claude Desktop configuration (claude_desktop_config.json):
{
"mcpServers": {
"infinigen": {
"command": "node",
"args": ["/path/to/infinigen-mcp/dist/index.js"]
}
}
}Development
# Watch mode for development
npm run watch
# Run in development mode
npm run devArchitecture
This MCP server acts as a wrapper around Infinigen's command-line interface, providing:
Tool Interface: MCP tools for scene generation operations
Process Management: Handles Infinigen subprocess execution
Output Handling: Manages generated files and provides access to results
Configuration Management: Simplifies Infinigen's configuration system
Available Tools
(To be implemented)
generate_nature_scene: Generate outdoor natural environmentsgenerate_indoor_scene: Create interior spacesgenerate_asset: Produce individual 3D objectslist_outputs: View generated scene filesconfigure_scene: Set scene parameters
Contributing
Contributions are welcome! This is an open-source project aimed at making Infinigen more accessible through AI assistants.
License
MIT
Acknowledgments
Infinigen by Princeton Vision & Learning Lab
Model Context Protocol by Anthropic
Related Links
Available Tools
3 toolscheck_infinigenB
Check if Infinigen is properly installed and accessible
Inputs: no named parameters.
generate_indoor_sceneC
Generate a photorealistic indoor scene with furniture and decorations using Infinigen.
Inputs: output_folder (required), seed, configs, use_gpu.
generate_nature_sceneC
Generate a photorealistic natural outdoor scene using Infinigen. Creates terrain, vegetation, and natural elements.
Inputs: output_folder (required), seed, configs, use_gpu.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelogβ¦
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: one checks installation status, one generates indoor scenes, and one generates outdoor scenes. There is no overlap or ambiguity between these functions, making tool selection straightforward for an agent.
All tool names follow a consistent verb_noun pattern (check_infinigen, generate_indoor_scene, generate_nature_scene). The naming is uniform and predictable, using snake_case throughout without any deviations.
With only 3 tools, the server feels thin for a domain like scene generation, which might benefit from more operations (e.g., editing scenes, listing generated assets, or configuring parameters). However, it covers basic functionality without being excessive.
The tools cover installation checks and two core generation tasks (indoor and outdoor scenes), but there are notable gaps. Missing operations include scene editing, asset management, or parameter tuning, which could limit agent workflows in more complex scenarios.
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