Disentangled 3D Scene Generation with Layout Learning
(2402.16936)Abstract
We introduce a method to generate 3D scenes that are disentangled into their component objects. This disentanglement is unsupervised, relying only on the knowledge of a large pretrained text-to-image model. Our key insight is that objects can be discovered by finding parts of a 3D scene that, when rearranged spatially, still produce valid configurations of the same scene. Concretely, our method jointly optimizes multiple NeRFs from scratch - each representing its own object - along with a set of layouts that composite these objects into scenes. We then encourage these composited scenes to be in-distribution according to the image generator. We show that despite its simplicity, our approach successfully generates 3D scenes decomposed into individual objects, enabling new capabilities in text-to-3D content creation. For results and an interactive demo, see our project page at https://dave.ml/layoutlearning/
Overview
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Introduces a novel method for generating 3D scenes by automatically decomposing scenes into individual objects using disentanglement without supervision.
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Utilizes Neural Radiance Fields (NeRFs) alongside learned spatial layouts to independently manipulate objects within a coherent 3D scene.
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Demonstrates superior performance in generating detailed and accurately decomposed 3D scenes compared to existing baselines, validated through quantitative and qualitative evaluations.
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Speculates on future improvements in AI for creating more sophisticated 3D content by building on unsupervised learning of object properties and dynamic scene generation.
Disentangled 3D Scene Generation with Unsupervised Layout Learning
Introduction
The focus of advancements in artificial intelligence has often been on the ability to parse and understand complex scenes as a collection of individual entities or objects. This paper introduces a novel method for generating 3D scenes that leverages the concept of disentanglement, where scenes are automatically decomposed into their constituent objects without supervision. The approach extends the use of Neural Radiance Fields (NeRFs) from creating monolithic 3D representations to generating compositions of multiple objects that can be manipulated independently. A distinctive aspect of this work is its reliance on the priors learned by a large pretrained text-to-image model to guide the disentanglement process.
Overview of Method
The method proposed in this paper marks a significant step in 3D scene generation by defining objects as components that can be independently manipulated while maintaining a "well-formed" scene. This is achieved by optimizing multiple NeRFs, each representing a different object within a scene, alongside a set of layouts that determine the spatial arrangement of these objects. These layouts are varied and learned through the process, promoting a meaningful decomposition of the scene into identifiable objects. The scenes are further optimized to match the distribution of images generated from text descriptions, ensuring that the composed scenes are coherent and contextually relevant.
Technical Contributions
The paper makes several key contributions:
- Introduces an operable definition of objects as parts of a scene that can undergo independent spatial manipulations while preserving scene validity.
- Implements a novel architecture that allows for the generative composition of 3D scenes by learning a set of NeRFs along with their spatial layouts.
- Demonstrates the utility of the proposed method in various 3D scene generation and editing tasks without requiring explicit supervision in terms of object labels, bounding boxes, or external models.
Evaluation and Findings
Quantitative and qualitative evaluations underscore the effectiveness of the layout learning approach in generating detailed 3D scenes that are accurately decomposed into individual objects. The method outperforms existing baselines in terms of the meaningfulness of the object-level decomposition, as evidenced by comparisons using CLIP scores. The paper also showcases the flexibility of the approach through applications in scene editing and object arrangement, further validating the practical utility of the proposed method.
Practical Implications and Future Directions
This work presents a significant advancement in the text-to-3D domain, offering a new tool for the creation of complex, editable 3D scenes from textual descriptions alone. The ability to disentangle these scenes into constituent objects without any form of explicit supervision opens up new avenues for content creation, providing users with granular control over the components of their generated scenes.
Looking ahead, the paper speculates on future developments in AI that could build on this foundation, such as improved techniques for unsupervised learning of object properties and relationships or the integration of dynamic elements within generated scenes. The ongoing refinement of these methods holds promise not only for more sophisticated 3D content creation tools but also for advancing our understanding of the processes by which AI can interpret and manipulate complex environments.
Concluding Remarks
This paper represents a notable step forward in the generative modeling of 3D scenes, distinguished by its unsupervised approach to disentangling scenes into individual, manipulable objects. By leveraging the capabilities of pretrained text-to-image models in a novel architecture, the authors have opened new possibilities for the creative and practical applications of AI in 3D content generation. As the field continues to evolve, the principles and methods introduced here could play a significant role in shaping the future of generative AI and its intersection with 3D modeling and design.
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