Open-Universe Indoor Scene Generation using LLM Program Synthesis and Uncurated Object Databases

Alapfogalmak

Our method enables open-universe indoor scene generation through program synthesis, leveraging large language models and vision-language models.

Kivonat

The article presents a system for generating 3D indoor scenes from text prompts without limitations on room types or object categories. It utilizes pre-trained large language models to synthesize programs describing objects and spatial relations. The system retrieves 3D meshes from unannotated databases using vision-language models. Experimental evaluations show superior performance in both closed-universe and open-universe scene generation tasks.

Statisztikák

"Our method generates 3D indoor scenes from open-ended text prompts."
"Experimental evaluations show that our system outperforms generative models trained on 3D data for traditional, closed-universe scene generation tasks."
"Our system also outperforms a recent LLM-based layout generation method on open-universe scene generation."

Idézetek

"A living room for watching TV"
"A high-end mini restaurant"
"A witch’s room with a cauldron’"
"A Japanese living room"
"A dining room for one"
"A bedroom"
"An old-fashioned bedroom"

Főbb Kivonatok

Open-Universe Indoor Scene Generation using LLM Program Synthesis and Uncurated Object Databases

by Rio Aguina-K... : arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.09675.pdf

Mélyebb kérdések

How can the use of foundation models impact the future of virtual scene generation?

Foundation models, such as large language models (LLMs) and vision-language models (VLMs), have the potential to revolutionize virtual scene generation. These advanced AI systems are trained on vast amounts of data, enabling them to understand and generate complex scenes based on natural language descriptions. By leveraging foundation models, virtual scene generation can become more flexible, open-ended, and efficient.
One key impact is the ability to create diverse and realistic scenes without relying heavily on pre-existing datasets. Foundation models can synthesize scenes from open-ended text prompts, allowing for a broader range of creative possibilities in virtual environments. This flexibility enables users to describe unique scenes that may not conform to traditional categories or constraints.
Moreover, foundation models enhance the speed and accuracy of generating virtual scenes. The AI's understanding of natural language descriptions combined with its ability to retrieve relevant 3D objects from databases streamlines the scene creation process. This efficiency is crucial for applications like game development, architectural visualization, interior design tools, and training AI agents in simulated environments.
Overall, by harnessing the power of foundation models in virtual scene generation, we can expect advancements in realism, creativity, customization options, automation capabilities, and overall user experience across various industries.

What are the potential drawbacks of relying on large language models for program synthesis in this context?

While large language models (LLMs) offer significant benefits for program synthesis in tasks like indoor scene generation using natural language prompts,
there are several potential drawbacks associated with their usage:

Error Proneness: LLMs may produce errors or inaccuracies when synthesizing programs due to limitations in understanding context or handling ambiguous instructions.

Limited Interpretability: The inner workings of LLMs are often complex and difficult to interpret by humans which could lead to challenges in debugging or refining generated programs.

Data Bias: LLMs learn from massive datasets which might contain biases leading to biased outputs or reinforcing stereotypes within generated content.

Computational Resources: Training and utilizing LLMs require substantial computational resources making it inaccessible for smaller organizations or individuals with limited computing power.

Fine-tuning Requirements: Fine-tuning an LLM model for specific tasks like program synthesis requires expertise and extensive labeled data which could be resource-intensive.

6 .Ethical Concerns: There are ethical considerations around privacy violations if sensitive information is inadvertently included during training data processing.

How might the concept of an "open-universe" approach be applied

to other areas beyond indoor scene generation?
The concept of an "open-universe" approach has broad implications beyond indoor scene generation:
1 .Creative Writing: Writers could use this approach for generating plot ideas,
character descriptions,and settings based on unrestricted prompts leading
to more imaginative storytelling.
2 .Product Design: In product design processes,the open-universe approach
could help designers explore unconventional concepts,such as futuristic gadgets,
unique furniture designs,and innovative architecture styles
3 .Game Development: Game developers could utilize this methodfor creating
dynamic levels,characters,and quests without being constrained by predefined templates,resultingin more engaging gameplay experiences
4 .Education: Educators might employ an open-universe strategyto develop customized learning materials,promote critical thinking skills,and encourage students'creativity through diverse assignmentsand projects
5 .Healthcare Simulation: In healthcare simulation scenarios,care providerscan benefitfrom varied patient cases,disease presentations,and treatment optionsgenerated throughan open-universe framework,to enhance clinical decision-making skillsand preparednessfor real-world medical situations