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

Conference: CVPR 2025
arXiv: 2410.16770
Code: TBD
Area: 3D Scene Generation / Scene Representation
Keywords: Scene Representation, Program Synthesis, 3D Scene Generation, CLIP Embeddings, training-free

TL;DR

Introduces Scene Language—a new paradigm representing visual scenes using a triplet \(\Phi(s)=(W,P,Z)\) of programs (\(P\), encoding hierarchical structure), words (\(W\), semantic categories), and embeddings (\(Z\), visual identity). It generates scene representations from text/image inputs via training-free inference using Claude 3.5 Sonnet, supports traditional/neural/hybrid rendering, and outperforms existing representations such as scene graphs in 3D/4D scene generation quality and controllable editing.

Background & Motivation

Background: Scene representation is the foundation of 3D generation. Scene graphs only encode coarse-grained topologies of objects and relations, lacking visual details; implicit representations from diffusion models, though generating high-quality results, lack structured controllability.

Limitations of Prior Work: (a) Scene graphs cannot precisely encode hierarchical structures and repetitive patterns (e.g., the regular arrangement of 32 pieces on a chessboard); (b) Purely neural representations cannot be explicitly edited (changing one part requires regenerating the entire scene); (c) Programmatic representations (e.g., ShapeAssembly) do not support appearance modeling.

Key Challenge: A representation is needed that possesses both the structured controllability of programs and the visual fidelity of neural embeddings.

Goal: To design a scene representation that simultaneously encodes structures, semantics, and visual identities, while being capable of zero-shot inference from pretrained LMs.

Key Insight: Programs are naturally structured representations (hierarchy, loops, transformations), natural language provides semantic understanding, and CLIP embeddings capture visual identity—making them highly complementary.

Core Idea: \(\Phi(s) = (W, P, Z)\)—programs encode structures, words encode semantics, and embeddings encode appearance, with all three generated synergistically through zero-shot inference by LMs.

Method

Overall Architecture

Input text/image description \(\rightarrow\) Claude 3.5 Sonnet generates a Python script (implementing entity functions, defining scene structures) \(\rightarrow\) String parameters are converted to embeddings via the CLIP text encoder \(\rightarrow\) Programs are executed recursively to generate the scene \(\rightarrow\) A renderer (Mitsuba ray tracing / 3D Gaussian Splatting+SDS / Minecraft / MIGC diffusion) is selected to output images. The entire process is training-free.

Key Designs

  1. Triplet Scene Representation \(\Phi(s) = (W, P, Z)\):

    • Program P: A Domain-Specific Language (DSL) defines entity functions with 4 macro operations—call (calling entity functions), union (combining transformed entities), union-loop (for-loop combination of repeating entities), and transform (pairing entities with 3D affine transformation matrices). Entity functions recursively call each other to form a hierarchical structure. For example, a chessboard = chess pieces \(\times{}32\) + grid squares \(\times{}64\) + board frame.
    • Words W: Natural language category labels of entities (e.g., "pawn", "board"), offering semantics understandable by LMs.
    • Embeddings Z: Each entity corresponds to a vector in the OpenCLIP-ViT/H text embedding space, encoding visual identity. An entity function is formulated as \(f_w: (z, \gamma) \mapsto h\), where \(z\) represents its own embedding and \(\gamma=[z_2,...,z_J]\) represents descendant entity embeddings.
    • Design Motivation: \(P\) provides an editable structural skeleton, \(W\) enables LMs to comprehend and reason about the scene, and \(Z\) bridges the gap from semantics to vision.

  2. Training-Free Inference:

    • LMs (Claude 3.5 Sonnet) receive system prompts (DSL definitions + helper functions) + in-context examples \(\rightarrow\) generate Python scripts.
    • Numerical/string parameters are mapped to embedding vectors using the CLIP text encoder.
    • Image-conditioned inference: GroundingSAM segmentation + Textual Inversion obtains embeddings for each entity.
    • LM inference time is <1 minute per scene.

  3. Multi-Renderer Support:

    • Mitsuba Ray Tracing: Primitive shapes (cubes, spheres, cylinders), high-quality ray tracing, <1 minute.
    • 3DGS + SDS: Guided by ControlNet + MVDream, ~30 minutes per object (A5000 48GB).
    • Minecraft: Asset placement, voxel-based building style.
    • MIGC T2I: Feed-forward diffusion model + layout conditioning.

Loss & Training

Completely training-free—no models are trained. The method leverages the zero-shot in-context learning capability of a pretrained LM (Claude 3.5 Sonnet) + CLIP embeddings + pretrained renderers.

Key Experimental Results

Main Results (Text-to-3D scene generation: 9 numerical + 8 general prompts)

Metric Scene Language GraphDreamer MVDream
Alignment (User Preference) Significantly Best Inferior Inferior
CLIP Similarity Highest Low Low
Counting Accuracy Perfect 0.11 0.11

Image-to-Scene Generation

Metric Scene Language GraphDreamer
LPIPS (\(\downarrow\)) 0.681 0.811

Ablation Study (Editing tasks)

Configuration Effect Explanation
Full (P+W+Z) Best Complete triplet
No-P (excluding program) Drop Loses structured editing capabilities
No-W (random strings) Drop LM cannot comprehend semantics
No-P-No-W Further Drop Both P and W are indispensable for LM inference

Key Findings

  • Perfect counting accuracy vs. only 0.11 for GraphDreamer/MVDream—programmatic representations show an absolute advantage in precise combinations.
  • Ablation proves that both P and W are indispensable for LM inference: P provides the actionable structure, while W provides semantic concepts that the LM can reason about.
  • LPIPS is 0.681 vs. 0.811 for image-conditioned generation, showing embedding Z effectively preserves visual identities.
  • LM inference completes in <1 minute; the rendering bottleneck lies in SDS (~30 minutes per object).

Highlights & Insights

  • "Program-as-Scene": The representation paradigm is incredibly elegant—programs naturally encode structures like hierarchies, repetitions, and transformations, allowing for precise editing (e.g., scaling a spiral staircase radius by 80%, deleting brick blocks, or modifying fractal branch counts).
  • Triplet Complementary Design: P provides the skeleton (editable but without appearance), W provides the semantic bridge (enabling LMs to reason), and Z provides visual fidelity (capturing specific appearances)—none can be omitted.
  • Training-free yet high-quality: Utilizes the zero-shot capability of LMs and CLIP without requiring any 3D training data.

Limitations & Future Work

  • LM inference is highly sensitive to text phrasing—minor textual changes can lead to substantial quality discrepancies.
  • Image parsing exhibits high variance across runs, and often fails when spatial descriptions are ambiguous.
  • SDS renderers introduce bias (e.g., umbrellas failing to fold completely), leaving shape and texture control coupled.
  • The recursion of entity functions can become excessively deep for complex scenes, making it difficult for LMs to process correctly.
  • vs. Scene Graphs: Scene graphs only encode coarse-grained topologies and fail to precisely control repetition/hierarchy. Scene Language vastly outperforms in counting accuracy.
  • vs. ShapeAssembly: Supports appearance modeling (via embedding Z), whereas ShapeAssembly only handles geometric structures.
  • vs. Implicit Representations of Diffusion Models: Scene Language features explicit interpretable semantics + hierarchical structures, enabling precise local editing.

Rating

  • Novelty: ⭐⭐⭐⭐⭐ The triplet representation paradigm is entirely novel, representing a paradigm innovation in scene representation by combining programs, words, and embeddings.
  • Experimental Thoroughness: ⭐⭐⭐⭐ Covers 3D/4D generation, image conditioning, multi-renderers, and editing, but quantitative comparison baselines are somewhat limited.
  • Writing Quality: ⭐⭐⭐⭐⭐ Concepts are exceptionally clear, and the DSL design is highly refined.
  • Value: ⭐⭐⭐⭐⭐ Proposes a brand new paradigm for scene representation; the training-free approach holds immense practical value.