AI-Native 3D Pipelines with fal

Featured by Matt Workman and fal, this project explores how AI-generated worlds can move beyond isolated demos into practical realtime production workflows for games, filmmaking, and interactive environments.

The Workflow

Built around Neilson Koerner-Safrata’s open-source IMAGE-BLASTER toolkit, the pipeline starts from a single image and generates:

• Explorable Gaussian splat environments
• Editable 3D meshes and physics objects
• Ambient soundscapes and spatial assets
• Unreal- and Blender-ready production scenes

Instead of treating AI generation as the final output, the workflow focuses on turning generated worlds into editable assets that can flow directly into realtime production pipelines.

How It Works

Claude Code orchestrates multiple generation systems together — coordinating Marble, fal, mesh generation, image cleanup, and sound synthesis through structured AI workflows.

Dynamic objects are extracted from the source image into standalone meshes while Marble reconstructs the remaining environment into an explorable Gaussian splat world. Those assets can then be refined, stitched together, or integrated into realtime engines and cinematic workflows.

The walkthrough demonstrates how IMAGE-BLASTER can function less as a standalone generation demo and more as infrastructure for AI-native 3D production pipelines.

Stack

• Marble 1.1 for world generation and Gaussian splat reconstruction
• IMAGE-BLASTER for orchestration and pipeline workflows
• Claude Code for workflow automation
• fal + Hunyuan 3D for mesh generation
• Blender and Unreal Engine for downstream editing and integration
• ElevenLabs SFX for ambient and object-based sound generation

What’s Next

• Faster image-to-world workflows for virtual production
• Expanded AI-native game development pipelines
• Better scene understanding and object extraction
• More seamless interoperability between generated worlds and traditional DCC tools

Full walkthroughs and implementation demos are available online.