SceneCraft: Layout-Guided 3D Scene Generation (2410.09049v2)

Abstract: The creation of complex 3D scenes tailored to user specifications has been a tedious and challenging task with traditional 3D modeling tools. Although some pioneering methods have achieved automatic text-to-3D generation, they are generally limited to small-scale scenes with restricted control over the shape and texture. We introduce SceneCraft, a novel method for generating detailed indoor scenes that adhere to textual descriptions and spatial layout preferences provided by users. Central to our method is a rendering-based technique, which converts 3D semantic layouts into multi-view 2D proxy maps. Furthermore, we design a semantic and depth conditioned diffusion model to generate multi-view images, which are used to learn a neural radiance field (NeRF) as the final scene representation. Without the constraints of panorama image generation, we surpass previous methods in supporting complicated indoor space generation beyond a single room, even as complicated as a whole multi-bedroom apartment with irregular shapes and layouts. Through experimental analysis, we demonstrate that our method significantly outperforms existing approaches in complex indoor scene generation with diverse textures, consistent geometry, and realistic visual quality. Code and more results are available at: https://orangesodahub.github.io/SceneCraft

SceneCraft: Layout-Guided 3D Scene Generation

The paper "SceneCraft: Layout-Guided 3D Scene Generation" introduces a sophisticated framework for generating high-quality 3D indoor scenes that conform to user-specified layouts and textual descriptions. This method addresses the inherent limitations of traditional text-to-3D approaches, which often struggle with complex scene generation that requires precise spatial and semantic composition.

Methodology

SceneCraft operates through a two-stage process:

1. User-Friendly Semantic-Aware Layout Control: A significant advancement in SceneCraft is its introduction of the "bounding-box scene" (BBS) format. This setup utilizes 3D bounding boxes as a guiding structure for scene layouts, allowing users to easily design intricate room arrangements. This approach circumvents the limitations seen in prior studies that restrict generation to small compositions. The BBS format supports large-scale, complex environments, offering users control over shape and object placement akin to building in the game Minecraft.
2. High-Quality Scene Generation with a 2D Diffusion Model: SceneCraft employs a 2D diffusion model, SceneCraft2D, which is conditioned on BBI (Bounding-Box Images) derived from BBS. It generates high-fidelity multi-view images conditioned on these layouts. The innovation lies in producing images without the constraints of panoramic representation, thus supporting diverse, irregular-shaped rooms and free camera trajectories. The SceneCraft model distills this 2D information into a cohesive 3D scene representation using a neural radiance field (NeRF).

Experimental Analysis

The paper provides strong empirical evidence demonstrating that SceneCraft outperforms existing methods in generating detailed indoor scenes. Key numerical results indicate superior performance in terms of visually compelling textures and consistent geometry. Additionally, experiments reveal SceneCraft's ability to manage more complex and free-form layouts compared to alternatives that rely on fixed camera perspectives or panoramic views.

Implications and Future Developments

From a practical standpoint, SceneCraft has significant implications for VR/AR applications and video game development, where the creation of realistic and customizable environments is crucial. Theoretically, this work advances the field of layout-guided scene generation by merging text-based inputs with spatial configurations, enhancing user control over the creative process.

Future developments could extend the system’s capabilities to outdoor or more dynamic environments, integrating functionalities for automatic layout creation. Furthermore, advancements may also focus on refining the fidelity of generated textures and incorporating more complex object interactions.

Conclusion

The research provides a compelling framework for layout-based 3D scene generation, emphasizing intuitive user control and high-quality output. SceneCraft represents a critical step forward in overcoming the challenges of text-to-3D scene synthesis, especially in generating large and intricately detailed indoor environments.