mlsanityData to 14 Oct 2026

ShellMaker: Language-Guided Exterior Completion under Structural Constraints

arXiv 2606.31680 ↗First posted 30 Jun 2026cs.CVcited by 2

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Abstract (arXiv)

Despite advances in indoor scene generation, synthesizing coherent building exteriors consistent with generated interiors remains largely unexplored. Existing methods can generate floor plans and wall layouts but typically stop at a structural shell, lacking stylistically consistent facades and roofs. Completing these exteriors is challenging because the footprint, wall geometry, and opening semantics must remain fixed-constraints that unconstrained generative models often violate. We introduce ShellMaker, a language-guided exterior completion framework that operates under these structural constraints. Given a building scaffold and a text style prompt, ShellMaker generates a complete exterior mesh with PBR materials by combining parametric roof generation, LLM-based part-aware prompt refinement, joint wall-roof material retrieval, and geometry-aware assembly. Operating on a format agnostic scaffold representation, ShellMaker generalizes to indoor generators, CityGML, and CAD inputs, while maintaining structural consistency and improving architectural coherence over retrieval and unconstrained generative baselines. The project page is available at https://ruiqixu37.github.io/ShellMaker_web/

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 25 of its 34 references that are in the map. First posted on arXiv 30 Jun 2026.

Builds on

25 of 34 references.

References · 25

  1. 2024Structured 3D Latents for Scalable and Versatile 3D Generationcited by 770
  2. 2023Holodeck: Language Guided Generation of 3D Embodied AI Environmentscited by 268
  3. 2025SAM 3D: 3Dfy Anything in Imagescited by 247
  4. 2025Native and Compact Structured Latents for 3D Generationcited by 175
  5. 2023RichDreamer: A Generalizable Normal-Depth Diffusion Model for Detail Richness in Text-to-3Dcited by 168
  6. 2025Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Materialcited by 165
  7. 2020SceneFormer: Indoor Scene Generation with Transformerscited by 158
  8. 2024Infinigen Indoors: Photorealistic Indoor Scenes using Procedural Generationcited by 137
  9. 2025Direct3D-S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attentioncited by 110
  10. 2025Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal Bridgingcited by 99
  11. 2025PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformerscited by 84
  12. 2023SparseFusion: Fusing Multi-Modal Sparse Representations for Multi-Sensor 3D Object Detectioncited by 83
13 more
  1. 2025HoloPart: Generative 3D Part Amodal Segmentationcited by 60
  2. 2025SceneWeaver: All-in-One 3D Scene Synthesis with an Extensible and Self-Reflective Agentcited by 56
  3. 2025ArtiScene: Language-Driven Artistic 3D Scene Generation Through Image Intermediarycited by 28
  4. 2024Coin3D: Controllable and Interactive 3D Assets Generation with Proxy-Guided Conditioningcited by 26
  5. 2025SceneEval: Evaluating Semantic Coherence in Text-Conditioned 3D Indoor Scene Synthesiscited by 26
  6. 2024SceneFactor: Factored Latent 3D Diffusion for Controllable 3D Scene Generationcited by 24
  7. 2021Building-GAN: Graph-Conditioned Architectural Volumetric Design Generationcited by 20
  8. 2025Seed3D 1.0: From Images to High-Fidelity Simulation-Ready 3D Assetscited by 20
  9. 2025Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generationcited by 14
  10. 2025Procedural Scene Programs for Open-Universe Scene Generation: LLM-Free Error Correction via Program Searchcited by 10
  11. 2025SpaceControl: Introducing Test-Time Spatial Control to 3D Generative Modelingcited by 9
  12. 2025BuildingBlock: A Hybrid Approach for Structured Building Generationcited by 3
  13. 2025SAT-SKYLINES: 3D Building Generation from Satellite Imagery and Coarse Geometric Priorscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.