BlockFusion: Generating Expandable and High-Quality 3D Scenes using Latent Tri-plane Extrapolation

Concepts de base

BlockFusion is a diffusion-based model that generates 3D scenes as unit blocks and seamlessly incorporates new blocks to extend the scene. It leverages a latent tri-plane representation and a denoising diffusion process to produce diverse, geometrically consistent, and unbounded large 3D scenes with high-quality shapes.

Résumé

The paper presents BlockFusion, a novel approach for generating expandable and high-quality 3D scenes. The key highlights are:

1. Training Pipeline:

   • The training data consists of 3D scene meshes that are randomly cropped into cubic blocks.
   • A per-block fitting process is used to convert the training blocks into tri-planes, which are then compressed into a more compact latent tri-plane space using an auto-encoder.
   • A denoising diffusion probabilistic model (DDPM) is trained on the latent tri-plane space to learn the distribution of 3D shapes.
   • A 2D layout conditioning mechanism is introduced to allow users to control the placement and arrangement of scene elements.

2. Scene Expansion:

   • To expand a scene, empty blocks are appended to overlap with the current scene.
   • The existing latent tri-planes are extrapolated to populate the new blocks by conditioning the denoising process on the overlapping regions.
   • The extrapolation is performed in the latent tri-plane space, producing semantically and geometrically meaningful transitions that seamlessly blend with the existing scene.

3. Experimental Results:

   • BlockFusion is capable of generating diverse, geometrically consistent, and unbounded large 3D scenes with unprecedented high-quality shapes in both indoor and outdoor scenarios.
   • The use of latent tri-plane representation and the proposed extrapolation mechanism are key to achieving high-quality and expandable 3D scene generation.

Stats

The training data consists of 3D scene meshes that are randomly cropped into cubic blocks.
The size of the training blocks is adjusted to be large enough to enclose major objects in the scene.
The point set sizes for on-surface and off-surface sampling are ⋃︀Ω⋃︀= 100000 and ⋃︀Ω0⋃︀= 500000, respectively.

Citations

"BlockFusion is capable of generating diverse, geometrically consistent and unbounded large 3D scenes with unprecedented high-quality shapes in both indoor and outdoor scenarios."
"The use of latent tri-plane representation and the proposed extrapolation mechanism are key to achieving high-quality and expandable 3D scene generation."

Idées clés tirées de

BlockFusion

by Zhennan Wu,Y... à arxiv.org 04-10-2024

https://arxiv.org/pdf/2401.17053.pdf

Questions plus approfondies

How can the proposed latent tri-plane extrapolation mechanism be extended to handle more complex scene expansion scenarios, such as adding new scene elements that do not overlap with the existing ones

The proposed latent tri-plane extrapolation mechanism can be extended to handle more complex scene expansion scenarios by incorporating advanced techniques in 3D shape generation and neural network architectures. One approach could involve integrating a hierarchical modeling system that allows for the generation of new scene elements that do not overlap with existing ones. This could be achieved by introducing a multi-scale representation of the scene, where different levels of detail are generated based on the proximity to existing elements. Additionally, incorporating semantic segmentation techniques could help identify regions in the scene where new elements can be added without disrupting the overall coherence. By leveraging advanced neural network models such as graph neural networks or attention mechanisms, the system can learn to extrapolate and generate new scene elements in a more context-aware and realistic manner.

What are the potential applications and use cases of the BlockFusion system beyond video games, such as in architecture, urban planning, or virtual reality experiences

The BlockFusion system has a wide range of potential applications beyond video games, particularly in fields such as architecture, urban planning, and virtual reality experiences. In architecture, BlockFusion can be used to rapidly prototype and visualize architectural designs in 3D, allowing architects and designers to explore different spatial configurations and layouts. Urban planning can benefit from BlockFusion by enabling city planners to generate and visualize large-scale urban environments, test different infrastructure layouts, and simulate the impact of urban development projects. In virtual reality experiences, BlockFusion can be used to create immersive and interactive virtual worlds for training simulations, educational purposes, and entertainment applications. By enabling the generation of high-quality and diverse 3D scenes, BlockFusion opens up possibilities for innovative applications in various industries.

Given the high-quality 3D scene generation capabilities of BlockFusion, how could the system be further improved to enable real-time interaction and manipulation of the generated scenes

To enable real-time interaction and manipulation of the generated scenes, the BlockFusion system could be further improved by integrating interactive features and real-time rendering capabilities. One approach could involve incorporating a user interface that allows users to interact with the generated scenes, such as moving objects, changing textures, and adjusting lighting conditions. By implementing real-time physics simulations, users could interact with the scenes in a more dynamic and realistic manner, enabling actions like object collisions, gravity effects, and object deformations. Additionally, optimizing the rendering pipeline for efficient GPU processing and leveraging techniques like level of detail (LOD) rendering and occlusion culling can enhance the system's performance for real-time scene manipulation. By focusing on enhancing interactivity and responsiveness, BlockFusion can provide users with a more immersive and engaging experience in manipulating 3D scenes in real-time.