Text-to-hierarchical three-dimensional scene generation: a new approach for layered three-dimensional modeling from natural language

Rapid advancements in generative models have enabled substantial progress in text-to-3D scene synthesis. However, existing approaches often lack hierarchical structure and geometric consistency, limiting their use in complex, editable scene creation. To address this issue, an end-to-end hierarchical framework for text-to-3D scene generation is introduced. The main contribution of this study lies not in advancing a single generative model but in the design of a framework that synergistically integrates state-of-the-art components for video synthesis and mesh reconstruction. By decoupling scene semantics from dynamic camera trajectory control, the proposed system efficiently produces structured object-level editable three-dimensional scenes from natural language. Comprehensive experiments showed that the proposed integrated approach achieved superior scene quality, consistency, and editing flexibility compared with existing methods, offering a powerful solution for applications, such as virtual reality and digital twins.

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Publication Details

Journal
Visual Computing for Industry Biomedicine and Art
Published
2026-08-26
DOI
https://doi.org/10.1186/s42492-026-00226-0
Primary Topic
Generative Adversarial Networks and Image Synthesis
Type
article
Field-Weighted Citation Impact
0.00
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article

Text-to-hierarchical three-dimensional scene generation: a new approach for layered three-dimensional modeling from natural language

Zuan Gu, Huimin Liu, Tianhan Gao, Lanxu Zhao
Visual Computing for Industry Biomedicine and Art
Generative Adversarial Networks and Image Synthesis
article

Text-to-hierarchical three-dimensional scene generation: a new approach for layered three-dimensional modeling from natural language

Zuan Gu, Huimin Liu, Tianhan Gao, Lanxu Zhao
article en

Abstract

Rapid advancements in generative models have enabled substantial progress in text-to-3D scene synthesis. However, existing approaches often lack hierarchical structure and geometric consistency, limiting their use in complex, editable scene creation. To address this issue, an end-to-end hierarchical framework for text-to-3D scene generation is introduced. The main contribution of this study lies not in advancing a single generative model but in the design of a framework that synergistically integrates state-of-the-art components for video synthesis and mesh reconstruction. By decoupling scene semantics from dynamic camera trajectory control, the proposed system efficiently produces structured object-level editable three-dimensional scenes from natural language. Comprehensive experiments showed that the proposed integrated approach achieved superior scene quality, consistency, and editing flexibility compared with existing methods, offering a powerful solution for applications, such as virtual reality and digital twins.

Visual Computing for Industry Biomedicine and ArtVol. 9(1)
Northeastern University (CN)
Openalex Percentile: Top 12%
Generative Adversarial Networks and Image Synthesis
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Text-to-hierarchical three-dimensional scene generation: a new approach for layered three-dimensional modeling from natural language — Zuan Gu, Huimin Liu, et al. · Visual Computing for Industry Biomedicine and Art (2026) | TGRS Research Map | TGRS