RealmDreamer: High-Fidelity 3D Scene Generation from Text Prompts with Inpainting and Depth Diffusion

核心概念

RealmDreamer generates high-quality 3D scenes from text prompts by leveraging pretrained 2D inpainting and depth diffusion models to produce scenes with parallax, detailed appearance, and accurate geometry.

要約

The paper introduces RealmDreamer, a technique for generating general forward-facing 3D scenes from text descriptions. The key insights are:

Robust initialization of a 3D Gaussian Splatting (3DGS) representation by leveraging 2D diffusion priors and monocular depth estimation.
A framework for learning consistent 3D scenes using 2D inpainting diffusion models, which fill in disoccluded regions while preserving the overall scene structure.
Incorporation of a depth diffusion model to improve the geometric accuracy of the generated 3D scenes.
A finetuning stage that further refines the 3D model's coherence and sharpness.

The method is able to generate high-quality 3D scenes with parallax, detailed appearance, and accurate geometry, outperforming state-of-the-art baselines.

統計

"Our technique does not require video or multi-view data and can synthesize a variety of high-quality 3D scenes in different styles, consisting of multiple objects."
"Trained on a large corpus of data, text-conditioned 2D diffusion models have shown excellent use as general purpose priors for a variety of tasks in computer vision, such as generation, editing, classification, and segmentation."

引用

"To address this problem, we introduce RealmDreamer, a method for high-fidelity generation of 3D scenes from text prompts."
"Our key insight is to use pretrained inpainting and depth priors with a robust initialization of a 3D Gaussian Splatting model, to produce scenes that can be rendered from a wide baseline."

抽出されたキーインサイト

RealmDreamer

by Jaidev Shrir... 場所 arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.07199.pdf

深掘り質問

How could the proposed technique be extended to handle 360-degree scene generation from text prompts?

To extend the proposed technique for 360-degree scene generation, several modifications and additions could be made:

Camera Trajectories: Implementing camera trajectories that cover a full 360-degree view around the scene would be essential. This would involve generating multiple views from different angles to capture the entire scene.
Incorporating Multi-View Consistency: Ensuring consistency across all views is crucial for a seamless 360-degree scene. Techniques like multi-view inpainting and depth estimation could be employed to fill in missing information and maintain coherence.
Adapting the 3D Representation: The 3D Gaussian Splatting representation used in the current technique would need to be modified to handle a full 360-degree view. This may involve adjusting the initialization process and optimizing the representation for a wider range of viewpoints.
Handling Disocclusions: Dealing with occlusions and disocclusions in a 360-degree scene is more complex. Advanced inpainting and depth diffusion models could be utilized to address these challenges effectively.

What are the potential limitations of using pretrained 2D diffusion models as priors, and how could these be addressed?

Using pretrained 2D diffusion models as priors may have the following limitations:

Limited Spatial Understanding: 2D models may lack a comprehensive understanding of 3D spatial relationships, leading to inaccuracies in depth estimation and scene generation.
Overfitting to 2D Data: Pretrained models may be biased towards 2D image data, potentially affecting the quality of 3D scene synthesis.
Complex Scene Representation: Handling complex scenes with diverse objects and viewpoints may challenge 2D models' ability to provide accurate priors.

These limitations could be addressed by:

Fine-Tuning on 3D Data: Adapting pretrained models on 3D datasets to improve their understanding of spatial relationships and enhance performance in 3D scene generation.
Architectural Enhancements: Modifying the architecture of the diffusion models to incorporate 3D information and improve their ability to handle depth estimation and scene synthesis.
Data Augmentation: Augmenting the training data with diverse 3D scenes to help the models generalize better and capture the complexities of 3D environments.

How might the technique's performance and efficiency be further improved, e.g., through the use of more advanced diffusion models or architectural choices?

To enhance the performance and efficiency of the technique, several strategies could be implemented:

Advanced Diffusion Models: Utilizing state-of-the-art diffusion models with improved capabilities for inpainting, depth estimation, and scene synthesis can enhance the quality of generated scenes.
Architectural Refinements: Optimizing the architecture of the models for better scalability, faster convergence, and higher accuracy in 3D scene generation.
Multi-Scale Approaches: Incorporating multi-scale processing to handle details at different levels and improve the overall fidelity of the generated scenes.
Attention Mechanisms: Integrating attention mechanisms to focus on relevant parts of the scene during generation, enhancing the model's ability to capture important details.
Transfer Learning: Leveraging transfer learning techniques to fine-tune the models on specific datasets or tasks, improving their performance on scene synthesis from text prompts.