The Scene Language: Representing Scenes with Programs, Words, and Embeddings (2410.16770v2)

Abstract: We introduce the Scene Language, a visual scene representation that concisely and precisely describes the structure, semantics, and identity of visual scenes. It represents a scene with three key components: a program that specifies the hierarchical and relational structure of entities in the scene, words in natural language that summarize the semantic class of each entity, and embeddings that capture the visual identity of each entity. This representation can be inferred from pre-trained LLMs via a training-free inference technique, given text or image inputs. The resulting scene can be rendered into images using traditional, neural, or hybrid graphics renderers. Together, this forms a robust, automated system for high-quality 3D and 4D scene generation. Compared with existing representations like scene graphs, our proposed Scene Language generates complex scenes with higher fidelity, while explicitly modeling the scene structures to enable precise control and editing.

• The paper introduces a novel representation that combines programmatic structures, semantic labels, and neural embeddings to capture detailed scene hierarchies.
• It leverages pre-trained language models and a domain-specific language to decompose complex scene generation, enhancing synthesis and editing accuracy.
• Experimental results demonstrate superior 3D/4D scene fidelity and prompt alignment compared to traditional methods such as scene graphs.

The Scene Language: Representing Scenes with Programs, Words, and Embeddings

The paper "The Scene Language: Representing Scenes with Programs, Words, and Embeddings" proposes a novel approach for visual scene representation that integrates programmatic structure, semantic categorization, and expressive embeddings. This methodology introduces a structured model to capture and generate complex 3D and 4D scenes with increased fidelity and precision compared to prior representation techniques like scene graphs.

Scene Language Composition

The Scene Language consists of three principal components:

1. myProgColor: This is a programmatic approach that specifies the hierarchical and relational structures within a scene, capturing details like the arrangement and repetition of entities. This component is crucial for defining the scene's organization through a computational process.
2. myWordColor: These natural language elements assign semantic classifications to each entity within a scene, providing contextual understanding by referencing their semantic groups.
3. myEmbdColor: Neural embeddings that encode instance-specific characteristics such as geometry and texture. These embeddings enable the representation to capture visual identities at a more granular level.

Rendering and Inference

The Scene Language leverages pre-trained large LMs to infer its representation from text or image inputs without additional training. The inference module decomposes complex scene generation into manageable tasks using domain-specific languages (DSL). Rendering outputs can be facilitated through various rendering techniques, including neural, traditional, and hybrid graphics.

Experimental Evaluation and Results

The empirical results validate the Scene Language's robustness across multiple tasks such as text-conditioned scene generation, scene editing, and image-conditioned scene reconstruction. The Scene Language demonstrates superior performance in maintaining structural integrity while providing precise scene control compared to existing methods like GraphDreamer and MVDream. User studies further corroborate its effectiveness in terms of alignment to prompts and counting accuracy.

Implications and Future Directions

The integration of structural, semantic, and identity-based components into a unified representation offers significant enhancements in scene synthesis, editing, and fidelity. The formalization through DSL allows for flexible scene manipulation, which is particularly useful for applications that require detailed hierarchical modeling and precise control.

The theoretical implications devolve into the realms of visual understanding and scene interpretation, enhancing the capacity for AI systems to model and reason about complex environments. Looking ahead, advancements could involve exploring more sophisticated inference techniques, expanding the variety of renderers, and refining the interactions between components for even greater expressive power.

The Scene Language provides a comprehensive framework for future exploration of scene representation in AI, emphasizing the importance of capturing the nuanced and multifaceted nature of visual scenes. This paper's contribution will potentially spur further research into more adaptable and precise scene representation strategies.