TRELLIS
TRELLIS is a revolutionary AI model developed by Microsoft Research that generates high-quality 3D assets from text descriptions or single 2D images using a novel Structured Latent Diffusion architecture. Released in December 2024, TRELLIS represents a fundamental advancement in 3D content generation by operating in a structured latent space that encodes geometry, texture, and material properties simultaneously rather than treating them as separate stages. The model produces complete 3D meshes with detailed PBR (Physically Based Rendering) textures, enabling direct use in game engines, 3D rendering pipelines, and AR/VR applications without extensive manual post-processing. TRELLIS supports both text-to-3D generation where users describe desired objects in natural language and image-to-3D reconstruction where a single photograph is converted into a full 3D model with inferred geometry from occluded viewpoints. The structured latent representation ensures geometric consistency and prevents the common artifacts seen in other 3D generation approaches such as floating geometry, texture seams, and unrealistic proportions. TRELLIS outputs standard 3D formats including GLB and OBJ with UV-mapped textures, making integration with professional tools like Blender, Unity, and Unreal Engine straightforward. Released under the MIT license, the model is fully open source and available on GitHub. Key applications include rapid 3D asset prototyping for game development, architectural visualization, product design mockups, virtual staging for real estate, educational 3D content creation, and metaverse asset generation. The model particularly benefits indie developers and small studios who lack resources for traditional 3D modeling workflows.
Key Highlights
3D Model Generation from Text and Image
Capability to create detailed and textured 3D models from both text descriptions and a single image.
High-Detail Textured Meshes
Produces industry-standard textured mesh outputs with PBR material support for professional-ready models.
Multi-View Consistency
Advanced multi-view algorithm ensuring the generated 3D model looks consistent and correct from every angle.
Wide Format Support
Exports in common 3D formats like GLB, OBJ, and FBX for compatibility with Blender, Unity, and Unreal.
About
TRELLIS is a revolutionary AI model developed by Microsoft Research that generates high-quality 3D assets from a single 2D image. Released in December 2024, TRELLIS achieves groundbreaking results in image-to-3D conversion and offers rapid prototyping capabilities particularly suited for game development, product design, and virtual reality applications. The model provides significant advantages over other solutions in the field in both speed and quality.
TRELLIS's technical architecture is built on Structured LATents (SLAT), a novel representation format that encodes 3D geometry, texture, and rendering information in a structured latent space for efficient and high-quality generation. The training process utilizes Objaverse and similar large-scale 3D datasets. The diffusion-based generation pipeline follows a multi-stage process working from image to SLAT representations and then to mesh, texture, and radiance field outputs. The model supports two different input modes: text-to-3D and image-to-3D generation.
In terms of performance, TRELLIS delivers impressive results. Image-to-3D generation completes in approximately 12 seconds on an A100 GPU, representing a significant speed improvement compared to InstantMesh's 30-second duration. An F-Score of 0.473 is achieved on the GSO dataset, representing a notable improvement when compared to One-2-3-45's score of 0.311. Generated 3D models can be output in mesh, texture map, and radiance field formats.
TRELLIS finds applications in game development pipeline asset generation, e-commerce 3D product visualization, architectural visualization, virtual and augmented reality content creation, and digital twin generation. Its fast generation time provides significant time savings in iterative design processes. It offers an efficient solution particularly for projects requiring high-volume 3D content generation.
TRELLIS is available as open-source under the MIT license. Model weights, training code, and inference pipeline are accessible via GitHub. Built on PyTorch, it is optimized for NVIDIA GPUs. Demos and pre-trained models are available through Hugging Face. A user-friendly Gradio interface provides a browser-based 3D generation experience.
TRELLIS is a significant work demonstrating the power of structured latent representations in single-image 3D generation. Compared to Wonder3D's cross-domain attention approach and SPA3D's point cloud alignment technique, TRELLIS holds a competitive position in both speed and output quality. Microsoft Research's strong research infrastructure reflects the model's technical depth and continuous development potential. Its generation speed and multi-format output support make TRELLIS an ideal choice for professional workflows.
Looking more closely at TRELLIS's technical innovations, the advantages offered by the SLAT (Structured Latent) representation format over other approaches in the field become more apparent. SLAT encodes 3D space on a structured voxel grid, preserving both local geometric details and overall structural coherence. This representation format enables the diffusion model to operate effectively in 3D space and enhances both mesh quality and texture details of generated models. The model's multi-output format support is a significant advantage: users can obtain mesh, Gaussian splatting, and radiance field outputs from the same generation and select the format that suits their needs. TRELLIS also supports conditional generation, allowing guidance through text prompts or reference images. Microsoft Research's active development process on TRELLIS means continuous improvements and new feature additions. The project's high star count on GitHub and community participation serve as concrete indicators of the model's impact in the field, reflecting strong adoption among researchers and practitioners alike.
Use Cases
Game Asset Creation
Accelerating design iterations by creating rapid 3D asset prototypes during game development.
E-Commerce 3D Product Images
Creating 360-degree viewable 3D models of products for online stores.
Architectural Visualization
Creating prototypes for rapid 3D modeling and visualization of architectural concepts.
Education and Simulation
Producing rapid 3D object and scene models for educational materials and simulation environments.
Pros & Cons
Pros
- Innovative 3D generation with Microsoft's SLAT (Structured Latent) representation
- Output as Radiance Fields, 3D Gaussians, and mesh from single image
- Trained on 500K+ high-quality 3D models
- Research accepted as CVPR 2025 Spotlight
- PBR materials, transparency, and detailed texture support
Cons
- Requires Linux and minimum 24GB GPU memory
- H100 GPU recommended for full capacity TRELLIS.2
- Setup and operation require technical knowledge
- Not yet fast enough for real-time 3D generation
Technical Details
Parameters
Unknown
Architecture
Structured Latent Diffusion
Training Data
Objaverse
License
MIT
Features
- Text-to-3D
- Image-to-3D
- Textured meshes
- High detail
- GLB export
- Multi-view consistency
- PBR materials
Benchmark Results
| Metric | Value | Compared To | Source |
|---|---|---|---|
| Üretim Süresi (Image-to-3D) | ~12 saniye (A100) | InstantMesh: ~30 saniye | TRELLIS Paper (arXiv:2412.01506) |
| F-Score (GSO Dataset) | 0.473 | CRM: 0.402 | TRELLIS Paper |
| Novel View PSNR | 22.8 dB | LGM: 20.5 dB | Papers With Code |
| Mesh Kalitesi (Chamfer Distance) | 0.034 | TripoSR: 0.048 | TRELLIS Paper |
Available Platforms
Frequently Asked Questions
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