mlsanityData to 7 Oct 2026

Cloud-Native Generative AI for Automated Planogram Synthesis: A Diffusion Model Approach for Multi-Store Retail Optimization

arXiv 2601.00527 ↗First posted 2 Jan 2026cs.LGcited by 0

On the map: Diffusion models → 3D, point cloud and scene generation

Abstract (arXiv)

Planogram creation is a significant challenge for retail, requiring an average of 30 hours per complex layout. This paper introduces a cloud-native architecture using diffusion models to automatically generate store-specific planograms. Unlike conventional optimization methods that reorganize existing layouts, our system learns from successful shelf arrangements across multiple retail locations to create new planogram configurations. The architecture combines cloud-based model training via AWS with edge deployment for real-time inference. The diffusion model integrates retail-specific constraints through a modified loss function. Simulation-based analysis demonstrates the system reduces planogram design time by 98.3% (from 30 to 0.5 hours) while achieving 94.4% constraint satisfaction. Economic analysis reveals a 97.5% reduction in creation expenses with a 4.4-month break-even period. The cloud-native architecture scales linearly, supporting up to 10,000 concurrent store requests. This work demonstrates the viability of generative AI for automated retail space optimization.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. Builds on 6 of its 30 references that are in the map. First posted on arXiv 2 Jan 2026.

Builds on

6 of 30 references.

References · 6

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  4. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,665
  5. 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generationcited by 511
  6. 2020Serverless Applications: Why, When, and How?cited by 3

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026PolyLayout: Hierarchical VLM-Guided Layout Generation Beyond Rectangular Rooms
  2. 2026Directly from Alpha to Omega: Controllable End-to-End Vector Floor Plan Generation ↗
  3. 2025OptiScene: LLM-driven Indoor Scene Layout Generation via Scaled Human-aligned Data Synthesis and Multi-Stage Preference Optimization
  4. 2026Generative Floor Plan Design with LLMs via Reinforcement Learning with Verifiable Rewards
  5. 2025FloorPlan-DeepSeek (FPDS): A multimodal approach to floorplan generation using vector-based next room prediction
  6. 2025Co-Layout: LLM-driven Co-optimization for Interior Layout
  7. 2026Space Syntax-guided Post-training for Residential Floor Plan Generation
  8. 2026RetailBench: Benchmarking long horizon reasoning and coherent decision making of LLM agents in realistic retail environments