DreamScene360: Generating Immersive 360-Degree 3D Scenes from Text Prompts

核心概念

DreamScene360 presents a novel pipeline that generates high-quality, immersive 360-degree 3D scenes from text prompts, without constraints on the scene complexity or content.

摘要

DreamScene360 introduces a text-to-3D 360-degree scene generation pipeline that creates comprehensive 360-degree scenes for in-the-wild environments in a matter of minutes. The approach utilizes the generative power of a 2D diffusion model and prompt self-refinement to create a high-quality and globally coherent panoramic image. This panoramic image serves as the foundation for the 3D scene generation.
To transform the 2D panorama into a 3D scene, DreamScene360 initializes a geometric field and 3D Gaussians. Semantic and geometric correspondences are then employed as regularizations to optimize the 3D Gaussians, addressing the challenges posed by the single-view input and filling the gaps in the unseen regions.
The self-refinement process leverages GPT-4V to provide feedback and prompt revision suggestions, enhancing the visual quality and text-image alignment of the generated panoramas. This user-friendly feature eliminates the need for extensive prompt engineering, a common challenge in previous text-to-3D generation methods.
Experiments demonstrate that DreamScene360 outperforms the state-of-the-art in terms of global consistency and visual quality, providing a seamless, immersive experience for 360-degree 3D scene generation from text prompts.

統計資料

The paper does not provide any specific numerical data or metrics to support the key logics. The evaluation is primarily based on qualitative comparisons and discussions.

引述

The paper does not contain any striking quotes that support the key logics.

從以下內容提煉的關鍵洞見

DreamScene360

by Shijie Zhou,... 於 arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.06903.pdf

深入探究

How can DreamScene360 be extended to generate 3D scenes at higher resolutions for an even more immersive user experience?

To generate 3D scenes at higher resolutions, DreamScene360 can implement techniques such as progressive refinement or hierarchical modeling. By progressively refining the generated scenes at lower resolutions, the model can iteratively enhance details and textures to achieve higher resolution outputs. Additionally, incorporating hierarchical modeling can allow the system to generate scenes in multiple levels of detail, starting from coarse representations and gradually adding finer details as needed. This approach can ensure that the generated scenes are immersive and visually appealing at higher resolutions.

How would DreamScene360 perform on more complex or abstract text prompts that go beyond simple scene descriptions?

DreamScene360's performance on complex or abstract text prompts can be enhanced by incorporating more advanced natural language processing (NLP) techniques. By leveraging state-of-the-art language models and text understanding algorithms, the system can better interpret and generate 3D scenes based on intricate or abstract descriptions. Additionally, integrating semantic parsing and context-aware processing can help the model capture the nuances and subtleties of complex text prompts, enabling it to generate more accurate and detailed 3D scenes.

What other applications or domains could benefit from the panoramic image-based approach used in DreamScene360, beyond 3D scene generation?

The panoramic image-based approach used in DreamScene360 can have applications in various domains beyond 3D scene generation. Some potential areas where this approach could be beneficial include:

Virtual Tours and Tourism: Creating immersive virtual tours of real-world locations or historical sites by generating 360° panoramic scenes from text descriptions.
Architectural Visualization: Generating realistic 3D architectural visualizations from textual descriptions of building designs or interior layouts.
Gaming and Simulation: Developing interactive gaming environments or simulation scenarios by converting text prompts into panoramic 3D scenes.
Education and Training: Enhancing educational materials with interactive 3D visualizations generated from textual content for better engagement and understanding.
Marketing and Advertising: Creating immersive product showcases or virtual experiences based on textual descriptions for marketing campaigns.

By applying the panoramic image-based approach in these domains, it can enable the creation of engaging and realistic visual experiences from textual inputs.