Communication Dans Un Congrès Année : 2026

Building-Diffusion: Graph Discrete Diffusion Model For Architectural Volumetric Design Generation

Résumé

Diffusion models have recently achieved remarkable suc- cess in continuous domains such as images and videos, yet applying them to discrete and structured data like graphs remains challenging. We propose Building-Diffusion, a conditional discrete diffusion model for node-attribute gen- eration in architectural building layout graphs. The method follows a two-stage pipeline: first, a self-supervised model encodes the input program graph (Gp) into a compact em- bedding; then, a discrete denoising network learns to it- eratively recover clean voxel labels from categorical noise while keeping the graph structure fixed. This design allows the model to capture categorical node distributions and pre- serve topological consistency. Experiments on the Building- GAN dataset demonstrate that Building-Diffusion produces layouts that are both realistic and structurally coherent. We further validate our approach through a user study and quantitative evaluations using three metrics. These results highlight discrete diffusion as a promising framework for graph-based generative modeling.

Fichier non déposé

Dates et versions

hal-05630881 , version 1 (22-05-2026)

Identifiants

  • HAL Id : hal-05630881 , version 1

Citer

Mohammed El Amine Sehaba, Igor Krawczuk, Serge Miguet, Xavier Marsault. Building-Diffusion: Graph Discrete Diffusion Model For Architectural Volumetric Design Generation. CV4AEC Workshop - CVPR 2026, Jun 2026, Denver (Colorado), United States. ⟨hal-05630881⟩
0 Consultations
0 Téléchargements

Partager

  • More