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December 18, 2025

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TRELLIS 2

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The method can handle complex structures, including open surfaces, non-manifold geometry, and enclosed interior structures, thus overcoming the limitations of iso-surface fields.

TRELLIS.2 also has the ability to model arbitrary surface attributes such as Base Color, Roughness, Metallic, and Opacity, thereby facilitating Physically Based Rendering (PBR) and photorealistic relighting.

This tool optimizes the pre- and post-processing of data for training and inference, allowing for quick conversions that are free from rendering and optimization.

It utilizes 'O-Voxel', a novel 'field-free' sparse voxel structure designed to encode detailed geometry and complex appearance simultaneously. A Sparse Compression VAE component is deployed to compress voxel data efficiently, encapsulating fully textured 3D assets into a compact representation with minimal perceptual degradation.

This enables efficient large-scale generative modeling. Its worth noting that TRELLIS.2 is purely a research project with Responsible AI considerations factored into all stages of its development.

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December 18, 2025

Initial release of TRELLIS 2.

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Pros and Cons

Pros

Open-source

High fidelity assets

Handles complex structures

Models surface attributes

Supports PBR

Photorealistic relighting

Efficient pre-post processing

O-Voxel encoding

Sparse Compression VAE

Minimal perceptual degradation

Large-scale generative modeling

Encodes geometry and appearance

Handles arbitrary topologies

Supports full PBR attributes

Efficient Compression

Instant image-to-3D conversion

Flexible Dual Grids representation

Compact structured latents

Overcomes iso-surface field limitations

1536 PBR textured assets

Supports Non-manifold geometry

Handles enclosed interior structures

Quick data conversions

Free from rendering optimization

Handles open surfaces

4B-parameter model

Encapsulates fully textured 3D assets

Voxel data compression

Outperforms vanilla DiTs

Handles Base Color, Roughness, Metallic, Opacity

Research-focused tool

Public datasets use

Minimalistic 3D asset processing

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Cons

Purely a research project

Requires high-end GPU

Limited application scope

Complex to implement

Potential data biases

No commercial use intended

Not user-friendly

Requires extensive technical knowledge

Dependent on NVIDIA H100

Absolute reliance on O-Voxel

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Q&A

What is TRELLIS.2?

TRELLIS.2 is an open-source image-to-3D model generator, designed to produce high fidelity textured assets using native 3D VAEs. Its core feature makes use of native and compact structured latents for providing both high fidelity and compression capabilities. This tool can handle complex structures, including open surfaces, non-manifold geometry, and enclosed interior structures, thus overcoming the limitations of iso-surface fields. It's a research project with Responsible AI considerations factored into all stages of its development.

How does TRELLIS.2 generate 3D models from images?

TRELLIS.2 generates 3D models from images through a process that begins with an Instant Bidirectional Conversion that transforms meshes into a new representation termed O-Voxel. The Sparse Compression VAE then encodes these voxels into a compact Structured Latent space. The result is a highly compact representation of a fully textured 3D asset with negligible perceptual degradation.

What are the key features of TRELLIS.2?

Key features of TRELLIS.2 include the ability to handle complex structures, model arbitrary surface attributes such as base colour, roughness, metallic, and opacity, and optimize pre and post-processing of data for training and inference. Additional features include the utilization of 'O-Voxel', a novel 'field-free' sparse voxel structure to encode detailed geometry and complex appearance simultaneously, and a Sparse Compression VAE component for efficient voxel data compression.

What kind of structures can TRELLIS.2 handle?

TRELLIS.2 can handle complex structures including open surfaces, non-manifold geometry, and enclosed interior structures.

How does TRELLIS.2 handle non-manifold geometry?

TRELLIS.2 handles non-manifold geometry by using a flexible dual grids representation within the O-Voxel structure. This methodology allows it to handle arbitrary topologies while preserving sharp edges.

What surface attributes can TRELLIS.2 model?

TRELLIS.2 can model arbitrary surface attributes such as Base Color, Roughness, Metallic, and Opacity.

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How does TRELLIS.2 facilitate Physically Based Rendering (PBR)?

TRELLIS.2 facilitates Physically Based Rendering (PBR) by allowing for the modeling of arbitrary surface attributes such as Base Color, Roughness, Metallic, and Opacity. It can hence, accurately model rich surface materials for PBR and carry out photorealistic relighting.

What is the purpose of the 'O-Voxel' in TRELLIS.2?

The purpose of the 'O-Voxel' in TRELLIS.2 is to provide a novel 'field-free' sparse voxel structure designed to encode both precise geometry and complex appearance simultaneously.

How does TRELLIS.2 compress voxel data?

TRELLIS.2 compresses voxel data using a Sparse Compression 3D VAE, employing a Sparse Residual Autoencoding scheme to directly compress voxel data into a compact structured latent space.

What are the benefits of using the Sparse Compression VAE component in TRELLIS.2?

The benefits of using the Sparse Compression VAE component in TRELLIS.2 are twofold: It efficiently and compactly encodes the fully textured 3D asset with minimal perceptual degradation and it enables efficient large-scale generative modeling.

How efficient is TRELLIS.2 in large-scale generative modeling?

TRELLIS.2 is highly efficient in large-scale generative modeling. It can encapsulate fully textured 3D assets into a compact representation with minimal perceptual degradation, thus enabling it to carry out efficient large-scale generative modeling.

What does it mean that TRELLIS.2 is an open-source project?

That TRELLIS.2 is an open-source project means it is freely available for the public to use, adapt, and improve upon. Any enhancements made to the source code can be shared back with the community, offering the opportunity for collaborative improvement.

How does TRELLIS.2 handle Responsible AI considerations?

In TRELLIS.2, Responsible AI considerations are factored into all stages of its development to ensure ethical use of AI. This includes using public datasets that have been reviewed to ensure there is no personally identifiable information or harmful content.

What is high fidelity textured assets generation in TRELLIS.2?

High fidelity textured assets generation in TRELLIS.2 refers to the creation of detailed, high-quality 3D assets from images, characterised by a high level of detail and realistic texture.

What is the meaning of 'native and compact structured latents' in the context of TRELLIS.2?

'Native and compact structured latents' in the context of TRELLIS.2 refers to the latent variables in its 3D VAE (Variational Autoencoder) model that encapsulate important, compact, and structured information about the input images for generating the 3D models.

Why is TRELLIS.2 considered a research project?

TRELLIS.2 is considered a research project because its development and applications are purely for the purpose of research. It explores cutting-edge technologies in 3D generation methodologies, and its developments contribute to the wider scientific and AI research community.

What is the process of pre- and post-processing of data in TRELLIS.2?

In TRELLIS.2, the process of pre and post-processing of data involves simple methods for training and inference that enable instant conversions. These conversions are fully rendering-free and optimization-free, leading to efficient conversions, and in turn, high-quality 3D model generation.

What is image-to-3D model generation as per TRELLIS.2?

Image-to-3D model generation as per TRELLIS.2 refers to the creation of a fully textured 3D asset from a two-dimensional image. It utilizes a number of advanced AI methodologies including native and compact structured latents, Sparse Compression VAE, and O-Voxel technology to generate these 3D models.

How does TRELLIS.2 overcome the limitations of iso-surface fields?

TRELLIS.2 overcomes the limitations of iso-surface fields by handling complex structures, including open surfaces, non-manifold geometry, and enclosed interior structures. It achieves this through the use of its O-Voxel technology which is designed to encode both detailed geometry and complex appearance simultaneously.

What is meant by '3D VAEs' in the context of TRELLIS.2?

'3D VAEs' in the context of TRELLIS.2 refer to 3D Variational Autoencoders. These are a type of generative model used in TRELLIS.2 to produce high fidelity textured assets by learning and encoding the structure of the input data into a set of latent variables.

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