3D Software Synthesis Driven by Constraint-Expressive Intermediate Representation
Shuqing Li, Anson Y. Lam, Yun Peng, Wenxuan Wang, Michael R. Lyu
Abstract
Graphical user interface (UI) software has undergone a fundamental transformation from traditional 2D interfaces to spatial 3D environments. While existing work has made remarkable success in 2D software generation, 3D software generation still remains underexplored. Current methods for 3D software generation usually generate 3D environment as a whole and cannot modify specific elements. Furthermore, these methods struggle to handle the complex spatial and semantic constraints inherent in the real world.
To address these challenges, we present Scenethesis, a novel requirement-sensitive 3D software synthesis approach that maintains formal traceability between user specifications and generated 3D software. Scenethesis is built upon ScenethesisLang, a domain-specific language that serves as a granular constraintaware intermediate representation to bridge natural language requirements and 3D software. It serves both as a comprehensive scene description language enabling fine-grained modification of 3D software elements and as a formal constraint-expressive specification language capable of expressing complex spatial constraints. By decomposing 3D software synthesis into stages, Scenethesis enables independent verification, targeted modification, and systematic constraint satisfaction. Our evaluation demonstrates that Scenethesis accurately captures over 80% of user requirements and satisfies more than 90% of hard constraints while handling over 100 constraints simultaneously. Furthermore, Scenethesis achieves a 42.8% improvement in BLIP-2 visual evaluation scores compared to the state-of-the-art method, establishing its effectiveness in generating high-quality 3D software that faithfully adheres to complex user requirements. Source code and supplemental materials are publicly available at https://sites.google.com/view/3d-software-synthesis.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext e653e23a-9bdd-4085-8870-26be3d506aa2Builds on31
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh et al.ICML 2021 · 47,906 citations
- BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language ModelsJunnan Li, Dongxu Li, Silvio Savarese, Steven C. H. HoiICML 2023 · 7,873 citations
- CLIPScore: A Reference-free Evaluation Metric for Image CaptioningJack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras et al.EMNLP 2021 · 937 citations
- LayoutGPT: Compositional Visual Planning and Generation with Large Language ModelsWeixi Feng, Wanrong Zhu, Tsu-Jui Fu, Varun Jampani et al.NeurIPS 2023 · 462 citations
- ATISS: Autoregressive Transformers for Indoor Scene SynthesisDespoina Paschalidou, Amlan Kar, Maria Shugrina, Karsten Kreis et al.NeurIPS 2021 · 293 citations
Related papers
- Scenethesis: A Language and Vision Agentic Framework for 3D Scene GenerationLu Ling, Chen-Hsuan Lin, Tsung-Yi Lin, Yifan Ding et al.ICLR 2026 · 74 citations
- SceneGenesis: 3D Scene Synthesis via Semantic Structural Priors and Mesh-Guided Video-Geometry FusionYueming Zhao, Hongyu Yang, Di HuangAAAI 2026
- SceneX: Procedural Controllable Large-Scale Scene GenerationMengqi Zhou, Yuxi Wang, Jun Hou, Shougao Zhang et al.AAAI 2025 · 22 citations
- Scenepainter: Semantically Consistent Perpetual 3D Scene Generation with Concept Relation AlignmentChong Xia, Shengjun Zhang, Fangfu Liu, Chang Liu et al.ICCV 2025 · 2 citations
- In Situ 3D Scene Synthesis for Ubiquitous Embodied InterfacesHaiyan Jiang, Leiyu Song, Dongdong Weng, Zhe Sun et al.ACM MM 2024 · 3 citations
