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

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arxiv 2410.16770 v2 pith:57S5RMXP submitted 2024-10-22 cs.CV cs.AI

Yunzhi Zhang , Zizhang Li , Matt Zhou , Shangzhe Wu , Jiajun Wu This is my paper
classification cs.CVcs.AI
keywords scenelanguagescenesvisualembeddingsentityidentityrepresentation
verification ladder T0 review T1 audit T2 compute T3 formal
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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 language models 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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Genie Sim 3.0 : A High-Fidelity Comprehensive Simulation Platform for Humanoid Robot

    cs.RO 2026-01 unverdicted novelty 6.0 of 10

    Genie Sim 3.0 introduces an LLM-powered scene generator, the first LLM-based automated evaluation benchmark, and a large open synthetic dataset that demonstrates zero-shot sim-to-real transfer for robotic manipulation...

  2. IR3D-Bench: Evaluating Vision-Language Model Scene Understanding as Agentic Inverse Rendering

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A benchmark that scores vision-language models by reconstructing the 3D scene behind an image as executable Blender code finds the models fail mainly on spatial precision, not tool usage.

  3. ReSpace: Text-Driven Autoregressive 3D Indoor Scene Synthesis and Editing

    cs.CV 2025-06 conditional novelty 6.0 of 10

    ReSpace is an autoregressive LLM framework for text-driven 3D indoor scene editing and synthesis, using a structured JSON scene representation and a voxelization-based layout metric.

  4. ArtiScene: Language-Driven Artistic 3D Scene Generation Through Image Intermediary

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A training-free pipeline that generates editable 3D scenes from text by using a generated 2D image as an intermediary to extract object shapes, appearances, positions, and poses.

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