pith:2CIW7WAA
HetScene: Heterogeneity-Aware Diffusion for Dense Indoor Scene Generation
Decomposing objects into primary structural roles and secondary contextual roles enables a two-stage diffusion process that generates coherent dense indoor scenes.
arxiv:2605.13586 v1 · 2026-05-13 · cs.CV · cs.AI
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Claims
we propose HetScene, a heterogeneous two-stage generation framework that decouples indoor layout synthesis into Structural Layout Generation (SLG) and Contextual Layout Generation (CLG). SLG first generates globally coherent structural layouts with only primary objects conditioned on text descriptions, top-down binary room masks, and spatial relation graphs.
That objects can be reliably decomposed into primary and secondary categories based on distinct roles in shaping a scene, and that this decomposition will resolve issues with dense arrangements and complex spatial dependencies.
HetScene proposes a two-stage heterogeneous diffusion framework that decomposes scenes into primary structural objects and secondary contextual objects to generate denser, more plausible indoor layouts.
References
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| First computed | 2026-05-18T02:44:23.155999Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/2CIW7WAAOYMSWMBIKFILOJ2CZ7 \
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Canonical record JSON
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