AnyHome: Open-Vocabulary Generation of Structured and Textured 3D Homes

Core Concepts

AnyHome enables the generation of detailed 3D indoor scenes from open-vocabulary text inputs, offering realism and customizability.

Abstract

The content introduces AnyHome, a framework for translating text into structured and textured indoor scenes. It focuses on generating diverse house-scale 3D environments with high realism. The process involves textual input modulation, structured geometry generation, and egocentric refinement. AnyHome stands out for its ability to create detailed geometries and textures that outperform existing methods quantitatively and qualitatively. Introduction: AnyHome aims to transform free-form textual narratives into realistic 3D indoor scenes. The framework offers extensive editing capabilities at varying levels of granularity. Previous research has struggled with creating robust 3D structures but AnyHome bridges this gap effectively. Methodology: AnyHome employs Large Language Models (LLMs) with designed templates for scene generation. The process includes Score Distillation Sampling for geometry refinement and egocentric inpainting for texture addition. Graph-based intermediate representations are used to describe the geometry structure. Results: AnyHome generates diverse scenes from open-vocabulary text inputs, showcasing versatility in design styles. The framework supports comprehensive editing capabilities, allowing modifications at various levels. Comparison with baselines shows superior performance in layout quality and content alignment.

Stats

"By prompting Large Language Models (LLMs) with designed templates, our approach converts provided textual narratives into amodal structured representations." "These represen-tations guarantee consistent and realistic spatial layouts by directing the synthesis of a geometry mesh within defined constraints." "AnyHome generates detailed geometries and textures that outperform existing methods in both quantitative and qualitative measures."

Quotes

"Imagine the possibilities if we could articulate our ideal living spaces in natural language and see them come to life." "Our method surpasses these direct LLM-generated plans, especially with abstract prompts, by preserving room relationships and accommodating diverse shapes and sizes."

Key Insights Distilled From

AnyHome

by Rao Fu,Zehao... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2312.06644.pdf

Deeper Inquiries

How can AnyHome's approach be applied to other domains beyond interior design?

AnyHome's approach can be extended to various domains beyond interior design by leveraging its open-vocabulary generation capabilities and structured scene synthesis. For example: Game Development: AnyHome can be used to generate diverse and realistic 3D environments for video games, providing developers with a tool to quickly create detailed game levels. Virtual Reality (VR) and Augmented Reality (AR): The framework can aid in creating immersive VR/AR experiences by generating interactive 3D scenes based on textual descriptions, enhancing user engagement. Training Simulations: AnyHome could be utilized in training simulations for industries like architecture, urban planning, or emergency response, enabling the creation of realistic scenarios for practice.

What are potential limitations or biases introduced by relying on Large Language Models (LLMs) for scene generation?

Data Biases: LLMs may reflect biases present in the training data, leading to biased outputs that perpetuate stereotypes or inequalities. Lack of Understanding Context: LLMs may struggle with understanding nuanced context or subtle details in textual inputs, potentially resulting in inaccurate interpretations. Complexity Handling: Generating complex scenes accurately might pose challenges as LLMs may struggle with intricate spatial relationships or abstract concepts.

How might the principles of environmental cognition influence future advancements in 3D scene generation technology?

Amodal Representations: Future advancements could focus on incorporating amodal representations into scene generation models to ensure consistent spatial layouts and enhance realism. Egocentric Exploration Techniques: Implementing egocentric exploration methods inspired by visual recording hypotheses could improve texture detailing and provide more lifelike appearances in generated scenes. User Interaction Design: By considering how individuals interact with their environment cognitively, future technologies could prioritize user-friendly interfaces that allow intuitive control over scene customization and editing processes.