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RealmDreamer: Text-Driven 3D Scene Generation with Inpainting and Depth Diffusion (2404.07199v2)

Published 10 Apr 2024 in cs.CV, cs.AI, cs.GR, and cs.LG

Abstract: We introduce RealmDreamer, a technique for generating forward-facing 3D scenes from text descriptions. Our method optimizes a 3D Gaussian Splatting representation to match complex text prompts using pretrained diffusion models. Our key insight is to leverage 2D inpainting diffusion models conditioned on an initial scene estimate to provide low variance supervision for unknown regions during 3D distillation. In conjunction, we imbue high-fidelity geometry with geometric distillation from a depth diffusion model, conditioned on samples from the inpainting model. We find that the initialization of the optimization is crucial, and provide a principled methodology for doing so. Notably, our technique doesn't require video or multi-view data and can synthesize various high-quality 3D scenes in different styles with complex layouts. Further, the generality of our method allows 3D synthesis from a single image. As measured by a comprehensive user study, our method outperforms all existing approaches, preferred by 88-95%. Project Page: https://realmdreamer.github.io/

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Citations (31)
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Summary

  • The paper introduces RealmDreamer, which combines 3D Gaussian Splatting initialization with 2D inpainting and depth diffusion to generate high-fidelity forward-facing 3D scenes.
  • It employs a multi-stage process that uses monocular depth estimation and fine-tuning to enhance scene geometry, appearance, and cohesion.
  • The method democratizes 3D content creation and opens new avenues for applications in virtual reality, gaming, and digital content research.

Text-Driven 3D Scene Generation with Inpainting and Depth Diffusion: An Overview of RealmDreamer

Introduction

The field of generative AI and, more specifically, text-based 3D scene synthesis has witnessed noteworthy advancements with the introduction of RealmDreamer. This technique represents a significant step in the evolution of 3D content creation, aiming to democratize the synthesis of high-fidelity 3D environments from text descriptions. Unlike prior methods that often struggle with generating cohesive and detailed scenes, RealmDreamer employs a combination of pretrained 2D inpainting and depth diffusion models, along with an innovative 3D Gaussian Splatting (3DGS) initialization approach. This method achieves state-of-the-art results in generating forward-facing 3D scenes that exhibit remarkable depth, detailed appearance, and realistic geometry, effectively addressing the limitations of existing text-to-3D techniques.

Methodology

RealmDreamer's methodology is distinctly structured into several stages, starting from a robust scene initialization to a fine-tuning phase that significantly enhances scene cohesiveness and detail:

  • Initialization with 3D Gaussian Splatting: RealmDreamer begins with an innovative initialization step that uses pretrained 2D priors to generate a reference image from a text prompt, which is then lifted into a 3D point cloud using state-of-the-art monocular depth estimation. The method effectively expands the point cloud by generating additional viewpoints, thereby enhancing the scene's initial geometric foundation.
  • Inpainting for Scene Completion: At this stage, RealmDreamer leverages 2D inpainting diffusion models to address disocclusions and fill in missing parts of the scene, guided by the text prompt. This process is meticulously designed to ensure that the inpainted regions seamlessly blend with the existing scene geometry, enhancing overall scene consistency.
  • Depth Diffusion for Enhanced Geometry: Incorporating a diffusion-based depth estimator, the technique refines the scene's geometric structure by conditioning on the samples from the inpainting model. This stage is pivotal in achieving high-fidelity depth perception within the generated scenes.
  • Finetuning for Enhanced Cohesion: The final phase involves finetuning the model with sharpened samples from image generators, further improving the scene's visual detail and coherence, ensuring alignment with the original text prompt.

Implications and Future Directions

RealmDreamer not only sets a new benchmark in text-driven 3D scene generation but also opens up new possibilities for research and application in the field of generative AI. The technique's ability to create detailed and cohesive 3D scenes from textual descriptions without the need for video or multi-view data can significantly impact various sectors including virtual reality, gaming, and digital content creation. Moreover, its generality and adaptability for 3D synthesis from a single image present further avenues for exploration.

Looking ahead, there are opportunities for refining the efficiency and output quality of RealmDreamer. Possible future developments could include the exploration of more advanced diffusion models for faster and more accurate scene generation, as well as innovative conditioning schemes that could enable the generation of 360-degree scenes with even higher levels of realism.

Conclusion

RealmDreamer represents a significant step forward in the field of text-to-3D scene synthesis, offering a novel and effective approach to creating high-fidelity, detailed 3D scenes from textual descriptions. By leveraging the capabilities of 2D inpainting and depth diffusion models within a structured methodology, RealmDreamer overcomes the limitations of existing techniques, opening new pathways for research and application in this fascinating domain of generative AI.

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