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pith:2026:2CIW7WAAOYMSWMBIKFILOJ2CZ7
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HetScene: Heterogeneity-Aware Diffusion for Dense Indoor Scene Generation

Cheng Peng, Chi Wang, Jiamin Xu, Junming Huang, Rong Zhang, Weiwei Xu, Zini Chen

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

C1strongest claim

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.

C2weakest assumption

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.

C3one line summary

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

30 extracted · 30 resolved · 4 Pith anchors

[1] Versatile rigid- fluid coupling for incompressible SPH.ACM Trans 2012 · doi:10.1145/2185520.2185552
[2] Human-centric Indoor Scene Synthesis Using Stochastic Grammar 2018 · doi:10.48550/arxiv.1808.08473
[3] 2024.doi:10.48550/arXiv.2406.11824 2024 · doi:10.48550/arxiv.2406.11824
[4] Make It Home: Automatic Optimization of Furniture Arrangement 2011 · doi:10.1145/2010324
[5] Interactive Furniture Layout Using Interior Design Guidelines 2011 · doi:10.1145/1964921
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First computed 2026-05-18T02:44:23.155999Z
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Signature Pith Ed25519 (pith-v1-2026-05) · public key
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d0916fd80076192b30285150b72742cffbc3e7971a6bcf37918ce1ca6e98f47c

Aliases

arxiv: 2605.13586 · arxiv_version: 2605.13586v1 · doi: 10.48550/arxiv.2605.13586 · pith_short_12: 2CIW7WAAOYMS · pith_short_16: 2CIW7WAAOYMSWMBI · pith_short_8: 2CIW7WAA
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/2CIW7WAAOYMSWMBIKFILOJ2CZ7 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: d0916fd80076192b30285150b72742cffbc3e7971a6bcf37918ce1ca6e98f47c
Canonical record JSON
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