Translating Words to Worlds: Zero-Shot Synthesis of 3D Terrain from Textual Descriptions Using Large Language Models

Round 1

Reviewer 1 Report

The paper presents a method for text-to-3D terrain model generation, which is more effective, more accurate, and more flexible in the editing capabilities of existing 3D models than standard approaches in this area.

The main contribution of the paper is based on the idea of using height maps as the basis for 3D terrain model generation. This allows bypassing diffusion models on which most of the current 3D terrain generation is based. The use of height maps enables most of the improvements that the proposed method makes possible.

The research described in this article is ingenious in uncovering and exploiting the capabilities for spatial awareness of existing LLMs, however, the spatial awareness of LLMs is still limited and overcoming this limitation is a focus for future research for the authors.  

The added flexibility and accuracy of 3D terrain generation is based on a more direct translation of textual prompts into height maps features. The proposed method is focused on updating existing 3D models by iteratively increasing their accuracy based on text prompts.  

The experimental results presented in the paper demonstrate that higher quality 3D terrain models can be generated with the proposed approach. The experiments and rationale, on which the better quality of the generated 3D models is assessed, are presented with clarity.  The data source is publicly available and documented properly in the paper.

The paper is well structured, the research is well presented, and advantages and disadvantages of the proposed research approach are well summarized.

The abstract offers a good and clear summary of the research. The current research landscape is well synthesized in the introduction allowing the reader to easily place in context the proposed research, while setting the background on which the research methods are based.  

Author Response

Dear Reviewer,

We are deeply grateful for your comprehensive and encouraging review of our research on direct text synthesis into 3D terrain. Your acknowledgment of the innovative aspects and scientific significance of our work is highly appreciated, especially considering the extensive time and effort we have devoted to this project.

We are pleased to learn that you find the method we presented not only feasible and promising but also superior in generating three-dimensional terrain models directly from text. Your positive feedback on the method’s quality and its potential for practical applications is particularly heartening.

In response to your insightful comments, we have expanded the Discussion section of our paper to include a more detailed exploration of potential future research directions. This addition aims to address the broader implications of our work and to outline possible pathways for further development and application of our method.

Thank you once again for your constructive feedback and for recognizing the value of our research. We are motivated by your comments to continue refining our approach and to explore new frontiers in the Text-to-3D domain.

Sincerely,
The Research Team

Reviewer 2 Report

In this article the results of research on direct text synthesis into 3D terrain without the need to use diffusion models are presented. By exploiting the inherent spatial awareness of a large language model, an innovative method was formulated to update existing 3D models with text, thereby increasing their accuracy. The research takes advantage of the spatial awareness capabilities of large language models and is divided into three stages. In the first stage, a Gaussian-Voronoi Map data structure is designed to generate complex terrain elevation maps from simple input data. Secondly, a Chain-of-Thought Behavior Tree strategy was created to extract terrain data from text data. Finally, a multi-agent based terrain feature adjustment method is introduced to support detailed editing and updating of generated terrains via text. The obtained experimental results indicate that this method efficiently interprets spatial information contained in the text and generates controlled 3D areas of excellent visual quality.

I am convinced that the research presented in this article is very interesting and has high scientific significance. It demonstrates that abandoning diffusion models and directly reading 3D data from text to generate 3D models is feasible and promising. I believe that the method, while providing the highest quality in the specialized task of generating three-dimensional terrain models in the Text-to-3D domain, offers highly flexible possibilities for secondary model editing. This method should be further developed, as it has significant potential for practical applications.

Author Response

Dear Reviewer,

Thank you for your insightful suggestions and constructive feedback on our paper. We greatly appreciate the time and effort you have invested in reviewing our work and providing such detailed recommendations for improvement.

We agree with your suggestion to expand the list of keywords and will include at least two more that accurately reflect the core aspects of our research. Regarding the numbering in the introduction, we will ensure it starts with 1, following the standard format and addressing your comment.

We also acknowledge the need for an expanded discussion and explanation within the paper. Accordingly, we will elaborate on the implications of our findings and provide a more comprehensive analysis of the results.

In line with your advice, we will enrich the Discussion section by exploring potential ways to enhance the method's efficiency and computational performance, especially for real-time or interactive applications. This will include considering various approaches to optimize the algorithm and reduce computational overhead.

Furthermore, we recognize the importance of robustness in our method and will discuss strategies to improve its resilience against text inputs with significant metric scale errors. We plan to develop a more sophisticated Chain of Thought strategy to better manage such scenarios, ensuring that our method remains reliable under varied conditions.

Once again, thank you for your valuable feedback. We are committed to refining our paper with these enhancements to better contribute to the field.

Sincerely,

The Research Team

Reviewer 3 Report

Somе suggеstions for improvеmеnt:

•             Kеywords should еxpand and includе at lеast 2 morе kеywords

•             Introduction should start with 1 not 0

•             Thеrе is a nееd to еxpand in discussions and еxplanations

•             Discussion should includе potеntial ways to improvе thе еfficiеncy and computational pеrformancе of thе mеthod and еspеcially for rеal timе or intеractivе applications.

•             Additionally, you can discuss ways to improvе thе robustnеss of thе mеthod whеn facеd with tеxt inputs containing significant mеtric scalе еrrors. This could involvе dеsigning a morе sophisticatеd Chain of Thought stratеgy to bеttеr handlе such casеs.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf