Trellis 3D AI is a next-generation AI model developed by Microsoft, focused on generating high-quality 3D assets directly from text or image prompts. It uses a powerful Structured LATent (SLAT) representation that can be decoded into multiple output formats, including Radiance Fields, 3D Gaussians, and meshes, and, backed by large-scale pretrained models, surpasses previous state-of-the-art methods in detail, generality, and consistency.
In terms of use, Trellis 3D AI is aimed at developers, designers, and artists who need to quickly generate 3D assets, and can be used in professional workflows such as game development, 3D printing, architectural visualization, product design, and animation production.
Main capabilities include:
- High-quality 3D asset generation: provides fine shapes and textures that surpass previous state-of-the-art solutions.
- Large-scale pretrained models: model scale of up to 2 billion parameters, trained on a diverse dataset of 500,000 objects, with broad coverage.
- Multi-format output: can generate Radiance Fields (for photorealistic rendering), 3D Gaussians (efficient storage and smooth geometric approximation), and traditional meshes that can be directly imported into most 3D modeling and game engines.
- Convenient editing and variants: after generation, 3D assets can be refined or customized locally, supporting seamless creation of variants and targeted modifications without regenerating the entire model.
- Text-to-3D generation: evolving support; future versions will allow the creation of high-quality 3D assets from text descriptions alone.
In terms of usage, installing Trellis 3D AI requires the following prerequisites: a Linux-based system, Python 3.8 or higher, an NVIDIA GPU with at least 16GB of VRAM, and a corresponding version of the CUDA Toolkit. When cloning the Trellis 3D AI repository from GitHub, use --recurse-submodules flag, then run the provided setup.sh script and pass in the required flags to install dependencies. If you need to adjust the CUDA version or attention backend, follow the detailed installation instructions in the repository documentation. The code has been verified on models such as NVIDIA A100 and A6000.
Applicable scenarios include: game development, 3D printing, architectural visualization, product design, animation production, and more. Trellis 3D AI is designed with scalability in mind, and its large-scale pretrained models and efficient pipeline can handle large-scale production scenarios, helping studios, game developers, and design companies significantly reduce asset creation costs.
In terms of limitations: system requirements are relatively high, requiring at least an NVIDIA GPU with 16GB of VRAM, along with a Linux system, Python 3.8+, and a compatible CUDA Toolkit; although it may run on other platforms or configurations, the above guidelines ensure optimal performance and reliability. The text-to-3D generation feature is still evolving and will only be available in future versions.
Demos, documentation, and community support are available on the Trellis 3D AI project page and the official GitHub repository, including installation guides, usage instructions, and interactive demos; you can also try a live demo on Hugging Face Spaces and participate in the community through GitHub issues, discussions, or social media forums. Future updates are planned to introduce a dedicated text-to-3D model, training code, and datasets.
