[PDF] I-Design: Personalized LLM Interior Designer | Semantic Scholar (2024)

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This work proposes LayoutGPT, a method to compose in-context visual demonstrations in style sheet language to enhance the visual planning skills of LLMs, and shows superior performance in converting challenging language concepts like numerical and spatial relations to layout arrangements for faithful text-to-image generation.

An automatic, versatile, and human-aligned evaluation metric for text-to-3D generative models is presented and Experimental results suggest the metric strongly align with human preference across different evaluation criteria.

GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs, is developed, a Transformer-based model pre-trained to predict the next token in a document which exhibits human-level performance on various professional and academic benchmarks.

The large potential of Objaverse is demonstrated via four diverse applications: training generative 3D models, improving tail category segmentation on the LVIS benchmark, training open-vocabulary object-navigation models for Embodied AI, and creating a new benchmark for robustness analysis of vision models.

Ind InstructScene, a novel generative framework that integrates a semantic graph prior and a layout decoder to improve controllability and fidelity for 3D scene synthesis, is introduced.

AnyHome is introduced, a framework that translates any text into well-structured and textured indoor scenes at a house-scale by prompting Large Language Models with designed templates and generates detailed geometries and textures that outperform existing methods in both quantitative and qualitative measures.

This work introduces WonderJourney, a modularized framework for perpetual 3D scene generation that starts at any user-provided location and generates a journey through a long sequence of diverse yet coherently connected 3D scenes, using an LLM to generate textual descriptions of the scenes in this journey.

The LucidDreamer, a domain-free scene generation pipeline by fully leveraging the power of existing large-scale diffusion-based generative model, produces Gaussian splats that are highly-detailed compared to the previous 3D scene generation methods, with no constraint on domain of the target scene.

This work introduces JARVIS-1, an open-world agent that can perceive multimodal input (visual observations and human instructions), generate sophisticated plans, and perform embodied control, all within the popular yet challenging open- world Minecraft universe.

This work proposes the first Large Reconstruction Model (LRM) that predicts the 3D model of an object from a single input image within just 5 seconds, and adopts a highly scalable transformer-based architecture with 500 million learnable parameters to directly predict a neural radiance field (NeRF) from the input image.

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