Generative Models

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Generative Models

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ReSpace: Text-Driven Autoregressive 3D Indoor Scene Synthesis and Editing
Jun 3, 2025Martin JJ. Bucher, Iro Armeni3D Scenes Generative Framework
A Jump-Diffusion Framework for Irregular Time Series Generation
May 29, 2025O. Pfohl, J. Chemseddine, P. Hagemann +3Synthetic Time Series Stochastic Systems
Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation
May 28, 2025Yi Zhang, Peng Wang, Difan ZouPartial Differential Equation Benchmarks Generative Models
PhyMAGIC: Physical Motion-Aware Generative Inference with Confidence-guided LLM
May 22, 2025Siwei Meng, Yawei Luo, Ping LiuLarge-Scale Video Generation Models 3D Generative Models
Riemannian Denoising Diffusion Probabilistic Models
May 7, 2025Zichen Liu, Wei Zhang, Christof Schütte +1Score-Based Diffusion Model Generative Models
Multimodal Language Models as Text-to-Image Model Evaluators
May 1, 2025Jiahui Chen, Candace Ross, Reyhane Askari-Hemmat +5Text-To-Image Evaluation Benchmarks
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Recursive Learning Without Collapse: A Weighting-Based Stabilization Framework
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DICE: Distilling Classifier-Free Guidance into Text Embeddings
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Low-dimensional adaptation of diffusion models: Convergence in total variation
Jan 22, 2025Jiadong Liang, Zhihan Huang, Yuxin ChenScore-Based Diffusion Model Denoising Diffusion Probabilistic Model
Active Learning Enables Generation of Molecules that Advance the Known Pareto Front
Jan 3, 2025Evan R. Antoniuk, Peggy Li, Nathan Keilbart +3Molecular Generation Molecular Property Prediction
Modeling Story Expectations: A Generative Framework using LLMs
Dec 13, 2024Hortense Fong, George Gui, Bo YangLlm-Generated Stories Narratives
Beyond-RAG: Question Identification and Answer Generation in Real-Time Conversations
Oct 14, 2024Garima Agrawal, Sashank Gummuluri, Cosimo SperaHievi-Rag Artificial Intelligence Assistants
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Jun 3, 2025 · cs.CVJ/K move · Enter open · S save

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

Martin JJ. Bucher, Iro Armeni

Stanford University

Scene synthesis and editing has emerged as a promising direction in computer graphics. Current trained approaches for 3D indoor scene generation either oversimplify object semantics through one-hot class encodings (e.g., 'chair' or 'table'), require masked diffusion for editing, ignore room boundaries, or rely on floor plan renderings that fail to capture complex layouts. LLM-based methods enable richer semantics via natural language, but lack editing functionality, are limited to rectangular layouts, or rely on weak spatial reasoning from implicit world models. We introduce ReSpace, a generative framework for autoregressive text-driven 3D indoor scene synthesis and editing. Our approach features a compact structured scene representation with explicit room boundaries that enables asset-agnostic deployment and frames scene manipulation as a next-token prediction task, supporting object addition, removal, and swapping via natural language. We employ supervised fine-tuning with a preference alignment stage to train a specialized language model for object addition that accounts for user instructions, spatial geometry, object semantics, and scene-level composition. We further introduce a voxelization-based evaluation metric capturing fine-grained geometric violations beyond 3D bounding boxes. Experiments surpass state-of-the-art on object addition and achieve superior human-perceived quality on the application of full scene synthesis, despite not being trained on it.

3D Scenes Generative Framework Boundaries

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