Trellis 3D

Trellis 3D AI supports multiple output formats including Radiance Fields for photorealistic rendering, 3D Gaussians for efficient storage and smooth geometry approximations, and traditional meshes that can be directly imported into most 3D modeling and game engines.

Trellis 3D AI requires a Linux-based system, Python 3.8 or higher, an NVIDIA GPU with at least 16GB memory, and the appropriate CUDA Toolkit version. The code has been verified on models like the NVIDIA A100 and A6000.

Yes, Trellis 3D AI is evolving to support text-to-3D generation. Future releases will allow users to create high-quality 3D assets solely from textual descriptions.

Trellis 3D AI allows you to refine or customize generated 3D assets locally, enabling seamless variant creation and tailored modifications without having to regenerate the entire model.

Trellis 3D AI distinguishes itself through its large-scale pre-trained models (up to 2 billion parameters), training on a massive dataset of 500K diverse objects, flexible output formats, and support for local edits.

Yes, Trellis 3D AI was built with scalability in mind. Its massive pre-trained models and efficient pipeline ensure it can handle large-scale production scenarios, making it ideal for studios, game developers, and design firms.

You can find demos, documentation, and community support on the Trellis 3D AI project page and official GitHub repository. These resources include installation guides, usage instructions, interactive demos, and videos showcasing Trellis 3D AI in action.

Future updates for Trellis 3D AI include dedicated text-to-3D models, training code, and datasets. These enhancements will further refine output quality, broaden format support, and improve integration options.