Interactive Scene Authoring with Specialized Generative Primitives (2412.16253v1)

Abstract: Generating high-quality 3D digital assets often requires expert knowledge of complex design tools. We introduce Specialized Generative Primitives, a generative framework that allows non-expert users to author high-quality 3D scenes in a seamless, lightweight, and controllable manner. Each primitive is an efficient generative model that captures the distribution of a single exemplar from the real world. With our framework, users capture a video of an environment, which we turn into a high-quality and explicit appearance model thanks to 3D Gaussian Splatting. Users then select regions of interest guided by semantically-aware features. To create a generative primitive, we adapt Generative Cellular Automata to single-exemplar training and controllable generation. We decouple the generative task from the appearance model by operating on sparse voxels and we recover a high-quality output with a subsequent sparse patch consistency step. Each primitive can be trained within 10 minutes and used to author new scenes interactively in a fully compositional manner. We showcase interactive sessions where various primitives are extracted from real-world scenes and controlled to create 3D assets and scenes in a few minutes. We also demonstrate additional capabilities of our primitives: handling various 3D representations to control generation, transferring appearances, and editing geometries.

• The paper introduces a novel framework of Specialized Generative Primitives that transforms casual video captures into detailed 3D scenes.
• It employs 3D Gaussian Splatting and Generative Cellular Automata for single-exemplar training and real-time generation with sparse voxel grids.
• The approach democratizes 3D content creation by enabling intuitive, semantically guided interaction for non-expert users.

Interactive Scene Authoring Using Specialized Generative Primitives

The paper "Interactive Scene Authoring with Specialized Generative Primitives" addresses the significant challenge of enabling non-expert users to author high-quality 3D scenes without the need for extensive expertise in complex 3D design tools. This is achieved through the introduction of Specialized Generative Primitives, a generative framework that leverages a combination of advanced techniques in computer vision and machine learning to simplify and enhance the process of 3D scene creation.

Methodology Overview

The core of the proposed framework is a robust pipeline designed to transform casual video captures into detailed 3D representations. This begins with 3D Gaussian Splatting, which converts environment captures into high-fidelity and explicit appearance models. Users interact with this model by selecting regions of interest that are guided by semantically-aware features derived from DINO—a self-supervised vision transformer model—features. This facilitates an intuitive selection process, allowing users to demarcate objects or scene areas efficiently.

To transition from discrete scene segments to versatile generative models, the paper employs an innovative approach using Generative Cellular Automata (GCA). This is adapted for single-exemplar training and controlled scene generation—a shift from traditional methods that require extensive datasets for effective performance. By focusing on sparse voxel grids during this process, the authors can decouple the generative task from the task of appearance modeling, ensuring the generation of both diverse yet contextually appropriate scene variations.

The final step in the pipeline involves a sparse patch consistency operation. This component ensures that the sparse voxel output from GCA is not only a coherent representation but also one that matches the user's selected regions at a high fidelity by linking to the pre-defined 3D Gaussians.

Noteworthy Results

The framework offers impressive real-time interaction capabilities, allowing users to train each primitive within approximately 10 minutes and generate new scene elements almost instantaneously. Moreover, the authors provide evidence of various creative sessions showcasing how these primitives can be extracted, controlled, and recomposed into new 3D assets and scenes. The results demonstrate the ability to handle various 3D representations, transfer appearances, and edit geometries, thereby significantly broadening the creative space available to users.

Implications and Speculations

The practical implications of integrating Specialized Generative Primitives into interactive 3D authoring are profound. This system empowers users without technical backgrounds to create complex scenes, thereby democratizing access to state-of-the-art 3D content creation methods. Theoretically, this aligns with ongoing trends towards more accessible AI-driven creative tools, which leverage user-friendly interfaces alongside powerful machine learning backends.

Looking forward, the framework opens doors to several exciting developments. Future research could focus on reducing the training and generation times even further, extending support to more complex scenes, or improving the generalization of primitives to unseen scenarios. Additionally, integrating this framework with virtual and augmented reality platforms could enhance immersive experiences and technical renderings in many creative and professional domains.

In summary, the paper pushes the boundaries of user-interactive 3D modeling, providing a practical, efficient, and highly scalable approach that holds great promise for both researchers and practitioners aiming to enhance the accessibility and creativity of digital content creation.