
VoxelForge
VoxelForge is a desktop voxel production tool for converting textured 3D meshes and animated FBX/GLB models into voxel assets, with VOX/GLB/FBX/OBJ export and optional experimental AI-assisted image-to-3D.
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About VoxelForge
VoxelForge is a desktop voxel production tool for converting textured 3D meshes and animated FBX/GLB models into voxel assets, with VOX/GLB/FBX/OBJ export and optional experimental AI-assisted image-to-3D.
About This Product
VoxelForge is a desktop software tool for voxel production, built for artists, game developers, technical artists and experimental 3D workflows.
A translated README.md file is included for each supported language, with additional setup notes, usage guidance, installation details and troubleshooting information.
It is designed to convert existing 3D assets, animated models and generated or reconstructed meshes into usable voxel-ready outputs.
This is not a game. It is a production tool for creating, testing and exporting voxel assets for use in external software and game engines.
Core Features
VoxelForge can convert textured 3D meshes into voxel models with adjustable resolution and export options for multiple pipelines.
Supported export formats include:
• VOX
• GLB
• FBX
• OBJ
• PNG palette export
The tool supports vertex colors, making exported voxel assets easier to use in real-time engines such as Unity and Unreal Engine.
Animation Voxelizer
VoxelForge includes an animation voxelization workflow for animated FBX and GLB files.
You can import a skeletal FBX or animated GLB, preview the original model and the voxelized result side by side, scrub through the animation, and export the animation as a frame sequence.
Animation voxelization includes:
• FPS, total frame and step-based sampling
• Source model and voxel preview
• Synced cameras
• Scrubbable timeline
• ZIP export
• Frame-by-frame VOX or GLB export
• Automatic skin baking per frame
This makes it possible to turn animated characters, props or prototypes into stylized voxel animation assets.
Material Transfer
VoxelForge can transfer visual information from the source model into the voxel output, including vertex color data and material-related properties such as metalness and specular values.
This helps voxelized assets keep more of their original visual identity when imported into other tools or engines.
Experimental AI-Assisted Image-to-3D Workflow
VoxelForge also includes an optional experimental image-to-3D workflow using local AI-assisted generation.
This feature is not required for the core voxelization workflow. It is intended as a prototyping layer for users who want to experiment with generated or reconstructed 3D meshes before converting them into voxel assets.
The CUDA-based AI generation workflow requires a high-end NVIDIA GPU. For this feature, an RTX 3090 with 24 GB of VRAM is the minimum recommended setup. It may also require additional local setup, a Hugging Face token for model downloads, Python/CUDA dependencies, and large model files.
All other core features of VoxelForge, including mesh voxelization, animation voxelization, preview, resolution adjustment, material transfer and export tools, are available on more moderate hardware configurations.
Results from the AI-assisted workflow depend heavily on input image quality, hardware and local configuration.
The goal of this feature is rapid prototyping, not one-click final production. Generated or reconstructed meshes can be voxelized, adjusted by resolution and exported as stylized, consistent assets.
Workflow Compatibility
VoxelForge is designed to fit into external creative and technical pipelines, including:
• Blender
• MagicaVoxel
• Goxel
• Unity
• Unreal Engine
• Custom game development workflows
Use Cases
VoxelForge can be used for:
• Voxelizing 3D props
• Converting animated models into voxel frame sequences
• Creating stylized game-ready voxel assets
• Rapid prototyping
• Testing different voxel resolutions
• Building voxel asset packs
• Experimenting with AI-assisted 3D-to-voxel workflows
• Preparing assets for real-time engines
Multilingual Interface
VoxelForge includes a multilingual interface with support for several languages, including English, French, Spanish, German, Italian, Portuguese, Russian, Japanese and Simplified Chinese.
A translated README.md file is provided for each supported language to help users understand the installation process, available workflows, optional AI setup and known limitations.
Important Note
The core voxelization features do not require generative AI or an RTX 3090. Standard mesh voxelization, animation voxelization, preview and export tools can run on more moderate hardware.
Only the optional CUDA-based AI-assisted image-to-3D workflow requires a high-end NVIDIA GPU. For that experimental feature, an RTX 3090 with 24 GB VRAM is the minimum recommended setup.
Please refer to the included README.md files for additional setup instructions, troubleshooting notes and language-specific guidance.
Game details
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VoxelForge — frequently asked questions
When does VoxelForge release?
Steam lists VoxelForge as coming soon without a confirmed date yet.
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System requirements
Minimum
- Requires a 64-bit processor and operating system
- OS: Windows 10 64-bit
- Processor: Intel Core i5 or AMD Ryzen 5 equivalent
- Memory: 8 GB RAM
- Graphics: GPU with WebGL/OpenGL support, 2 GB VRAM recommended
- Storage: 150 MB available space
- Additional Notes: Core voxelization features do not require generative AI. The optional experimental AI-assisted image-to-3D workflow requires additional local setup, a Hugging Face token for model downloads, and a high-end NVIDIA GPU.
Recommended
- Requires a 64-bit processor and operating system
- OS: Windows 10/11 64-bit
- Processor: Intel Core i7 / AMD Ryzen 7 or better
- Memory: 32 GB RAM
- Graphics: NVIDIA RTX 3090 or better, 24 GB VRAM recommended for the experimental AI image-to-3D workflow
- Storage: 15 GB available space
- Additional Notes: The AI-assisted image-to-3D feature is experimental and optional. It may require large model downloads, CUDA/Python dependencies, and a Hugging Face token. Results depend on input image quality and hardware.










