HoloDreamer: Holistic 3D Panoramic World Generation from Text Descriptions (2407.15187v1)

Abstract: 3D scene generation is in high demand across various domains, including virtual reality, gaming, and the film industry. Owing to the powerful generative capabilities of text-to-image diffusion models that provide reliable priors, the creation of 3D scenes using only text prompts has become viable, thereby significantly advancing researches in text-driven 3D scene generation. In order to obtain multiple-view supervision from 2D diffusion models, prevailing methods typically employ the diffusion model to generate an initial local image, followed by iteratively outpainting the local image using diffusion models to gradually generate scenes. Nevertheless, these outpainting-based approaches prone to produce global inconsistent scene generation results without high degree of completeness, restricting their broader applications. To tackle these problems, we introduce HoloDreamer, a framework that first generates high-definition panorama as a holistic initialization of the full 3D scene, then leverage 3D Gaussian Splatting (3D-GS) to quickly reconstruct the 3D scene, thereby facilitating the creation of view-consistent and fully enclosed 3D scenes. Specifically, we propose Stylized Equirectangular Panorama Generation, a pipeline that combines multiple diffusion models to enable stylized and detailed equirectangular panorama generation from complex text prompts. Subsequently, Enhanced Two-Stage Panorama Reconstruction is introduced, conducting a two-stage optimization of 3D-GS to inpaint the missing region and enhance the integrity of the scene. Comprehensive experiments demonstrated that our method outperforms prior works in terms of overall visual consistency and harmony as well as reconstruction quality and rendering robustness when generating fully enclosed scenes.

Overview of "HoloDreamer: Holistic 3D Panoramic World Generation from Text Descriptions"

The paper "HoloDreamer: Holistic 3D Panoramic World Generation from Text Descriptions" presents a novel framework that addresses the challenges of generating highly consistent, fully enclosed 3D scenes solely from text descriptions. The key innovations lie in leveraging advancements in text-to-image diffusion models and 3D Gaussian Splatting (3D-GS) to overcome the limitations of previous outpainting-based methods, which often struggled with global consistency and scene integrity.

Key Contributions

1. Novel 3D Scene Generation Approach:
   • The HoloDreamer framework introduces a two-stage process for generating holistic 3D scenes.
   • By initially producing a high-definition panorama and then reconstructing it with 3D-GS, the framework ensures view consistency and scene completeness, tackling the drawbacks of prior iterative outpainting methodologies.
2. Stylized Equirectangular Panorama Generation:
   • A major component of the framework is the stylized panorama generation pipeline that integrates multiple diffusion models.
   • This pipeline begins with the generation of a base panorama using a fine-tuned diffusion model, followed by style transfer and detail enhancement stages. Techniques such as lineart extraction and tile-controlled diffusion models are utilized to achieve high-quality and aesthetically consistent panoramas.
3. Enhanced Two-Stage Panorama Reconstruction:
   • The two-stage optimization of 3D-GS involves initial depth estimation and point cloud reconstruction, followed by a comprehensive multi-view constraint optimization process.
   • A filtered point cloud of the generated panorama initiates the reconstruction, while a subsequent inpainting stage ensures the integrity and robustness of the scene rendering.

Results and Implications

The experimental results robustly validate that HoloDreamer outperforms existing methods in several critical dimensions, including visual consistency, harmony, reconstruction quality, and rendering robustness. Quantitative metrics such as PSNR, SSIM, and LPIPS showcase superior performance in comparison to baseline models like Text2Room, Text2NeRF, and LucidDreamer.

The broader implications of these advances include significant enhancements in various domains requiring high-fidelity 3D scene generation. These range from virtual reality and gaming to film production and the burgeoning field of the metaverse, where the demand for realistic and coherent 3D content is ever-increasing. The holistic approach to scene generation proposed in HoloDreamer not only reduces the manual effort required in 3D modeling but also lowers the barrier to entry for newcomers by leveraging intuitive text descriptions.

Future Directions

Further research might delve into the following areas:

• Data Scarcity Improvement: Enhancing the diversity and complexity of generated panoramas requires larger and more varied datasets.
• Optimized Reconstruction: Introducing additional iterative inpainting stages and refining camera setup strategies could further balance reconstruction quality and efficiency.
• Generalization to More Complex Descriptions: Increased model training and more intricate text descriptors could improve the model's robustness and application to a broader array of 3D scenes.

Conclusion

The HoloDreamer framework marks a significant stride in text-driven 3D scene generation. Its dual emphasis on generating high-quality panoramas and ensuring robust, consistent scene reconstruction paves the way for more comprehensive applications in various industries reliant on advanced 3D content creation. Through comprehensive experiments and innovative methodologies, HoloDreamer establishes itself as a pivotal contribution to the progression of text-to-3D generation technologies.