TRELLISWorld: Training-Free World Generation from Object Generators

TL;DR

TRELLISWorld introduces a training-free, modular approach to generate large, coherent 3D scenes from text prompts by repurposing object diffusion models, enabling scalable and flexible scene creation without retraining.

Contribution

It reformulates scene generation as a multi-tile denoising problem using object diffusion models, eliminating the need for scene-level training or datasets.

Findings

Supports diverse scene layouts

Enables efficient scene synthesis

Allows flexible editing of 3D scenes

Abstract

Text-driven 3D scene generation holds promise for a wide range of applications, from virtual prototyping to AR/VR and simulation. However, existing methods are often constrained to single-object generation, require domain-specific training, or lack support for full 360-degree viewability. In this work, we present a training-free approach to 3D scene synthesis by repurposing general-purpose text-to-3D object diffusion models as modular tile generators. We reformulate scene generation as a multi-tile denoising problem, where overlapping 3D regions are independently generated and seamlessly blended via weighted averaging. This enables scalable synthesis of large, coherent scenes while preserving local semantic control. Our method eliminates the need for scene-level datasets or retraining, relies on minimal heuristics, and inherits the generalization capabilities of object-level priors. We demonstrate that our approach supports diverse scene layouts, efficient generation, and flexible editing, establishing a simple yet powerful foundation for general-purpose, language-driven 3D scene construction.