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microsoft/TRELLIS

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trellis3d.github.io↗

TRELLIS

TRELLIS is a 3D generative AI model and latent diffusion framework designed to transform natural language descriptions or reference images into textured 3D assets. It operates as a text-to-3D asset generator that utilizes structured latent representations to produce high-quality 3D meshes, Gaussians, and Radiance Fields.

The system functions as a multi-format 3D decoder, converting internal representations into standard exchange formats such as GLB and PLY. It also serves as a 3D asset editing tool, enabling the modification of specific regions of generated objects through targeted text or image-based prompts.

The framework covers a broad range of capabilities including cross-modal conditioning and diffusion-based latent generation. It supports large-scale model training across single or multi-node GPU configurations and provides workflows for creating visual variations of existing assets.

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Features

  • Text-to-3D Generators - Transforms natural language descriptions or reference images into high-quality textured 3D assets.
  • Diffusion-Based 3D Generators - Uses a diffusion process to predict 3D latent representations from text or image embeddings.
  • Latent Space Generative Models - Represents complex 3D geometry and texture as compressed tensors to enable scalable generation.
  • 3D Asset Generators - Synthesizes detailed 3D objects with textures using text descriptions or image conditions.
  • Generative 3D Modeling - Implements a deep learning framework for synthesizing 3D meshes, Gaussians, and Radiance Fields.
  • Distributed GPU Training - Scales model training across multiple GPU nodes using synchronized gradient updates.
  • Latent-to-Pixel Decoding - Converts internal compressed 3D representations into standard exchange formats like GLB and PLY.
  • Asset Variation Generators - Creates new versions of existing 3D assets that remain visually aligned with specified text prompts.
  • Large-Scale Model Training - Supports large-scale generative model training across single or multi-node GPU configurations.
  • Large Scale Training - Trains generative 3D models across single or multi-node GPU setups for improved quality.
  • Multi-Format Latent Decoding - Translates learned latent vectors into diverse outputs including 3D Gaussians, meshes, and radiance fields.
  • 3D Scene Exporters - Provides a pipeline to export internal 3D representations into standard exchange formats such as GLB and PLY.
  • 3D Asset Exporters - Converts generated 3D assets into standard industry file types such as GLB and PLY.
  • Global Asset Variations - Provides capabilities to modify generated 3D objects to create overall visual variations.
  • Local Region Editing - Enables modification of specific regions of a 3D model using targeted prompts to refine details.
  • Prompt-Based Geometry Editing - Provides tools for modifying specific regions of 3D objects through targeted text or image-based prompts.
  • 3D Representation Conversions - Converts internal 3D latent representations into various formats like meshes, 3D Gaussians, and Radiance Fields.
12,977 stars·1,254 forks·Python·MIT·27 views

Star history

Star history chart for microsoft/trellisStar history chart for microsoft/trellis

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does microsoft/trellis do?

TRELLIS is a 3D generative AI model and latent diffusion framework designed to transform natural language descriptions or reference images into textured 3D assets. It operates as a text-to-3D asset generator that utilizes structured latent representations to produce high-quality 3D meshes, Gaussians, and Radiance Fields.

What are the main features of microsoft/trellis?

The main features of microsoft/trellis are: Text-to-3D Generators, Diffusion-Based 3D Generators, Latent Space Generative Models, 3D Asset Generators, Generative 3D Modeling, Distributed GPU Training, Latent-to-Pixel Decoding, Asset Variation Generators.

Which projects share features with microsoft/trellis?

Projects with overlapping indexed features include: threestudio-project/threestudio — Threestudio is a 3D generative AI framework designed to create three-dimensional assets from text prompts and images.… openai/point-e — Point-e is a system for 3D model synthesis that generates three-dimensional point clouds from natural language… ashawkey/stable-dreamfusion — This project is a diffusion-based 3D generator and image-to-3D reconstruction system. It translates natural language… snowkylin/tensorflow-handbook — This project is a comprehensive educational resource and tutorial handbook for building, training, and deploying… coplaydev/unity-mcp — Unity MCP is a plugin that connects the Unity Editor to AI assistants through the Model Context Protocol, enabling… openai/shap-e — Shap-E is a generative 3D modeling system that creates three-dimensional digital assets from natural language…

Projects sharing features with TRELLIS

These projects share indexed features with TRELLIS. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • threestudio-project/threestudiothreestudio-project avatar

    threestudio-project/threestudio

    7,027View on GitHub↗

    Threestudio is a 3D generative AI framework designed to create three-dimensional assets from text prompts and images. It provides specialized pipelines for text-to-3D generation and image-to-3D reconstruction, utilizing a neural radiance field trainer to produce geometry and textures. The framework is distinguished by its support for hybrid geometry backends, including signed distance functions, tetrahedra grids, and volume grids. It employs score distillation sampling to guide the generation process and features a modular plugin system for loading custom modules and nodes. The system covers

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  • openai/point-eopenai avatar

    openai/point-e

    6,886View on GitHub↗

    Point-e is a system for 3D model synthesis that generates three-dimensional point clouds from natural language descriptions and two-dimensional images. It utilizes diffusion models to synthesize these spatial representations based on text prompts or source images. The project includes specialized tools for refining these outputs, such as a point cloud upsampler to increase the density and resolution of low-resolution models. It also provides a mesh converter that uses distance function regression to transform raw point cloud data into structured 3D meshes. The broader capability surface cove

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  • ashawkey/stable-dreamfusionashawkey avatar

    ashawkey/stable-dreamfusion

    8,841View on GitHub↗

    This project is a diffusion-based 3D generator and image-to-3D reconstruction system. It translates natural language descriptions or two-dimensional images into three-dimensional assets using neural radiance fields and diffusion models. The system utilizes score-distillation sampling and diffusion-based guidance to refine 3D shapes without requiring 3D training data. It includes specialized tools for transforming neural representations into exportable meshes with texture and material data, as well as a pipeline for iterative optimization of geometry and textures. The project covers a broad r

    Python
    View on GitHub↗8,841
  • snowkylin/tensorflow-handbooksnowkylin avatar

    snowkylin/tensorflow-handbook

    3,927View on GitHub↗

    This project is a comprehensive educational resource and tutorial handbook for building, training, and deploying machine learning models using TensorFlow 2. It serves as a structured learning guide covering core deep learning concepts, including neural network architectures, automatic differentiation, and tensor operations. The handbook provides technical guidance on optimizing execution efficiency through GPU memory management, distributed training, and model quantization. It also includes detailed manuals for constructing high-performance data pipelines and exporting models for production s

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