# iPLAN: Interactive and Procedural Layout Planning

arXiv: https://arxiv.org/abs/2203.14412 · HTML: https://mlsanity.com/paper/2203.14412/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 44 references that are in the map.
- First posted on arXiv 27 Mar 2022.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation](https://mlsanity.com/paper/2008.00951/) (2020)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Image Generation from Scene Graphs](https://mlsanity.com/paper/1804.01622/) (2018)
- [SEAN: Image Synthesis with Semantic Region-Adaptive Normalization](https://mlsanity.com/paper/1911.12861/) (2019)
- [Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models](https://mlsanity.com/paper/1811.12463/) (2018)
- [House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation](https://mlsanity.com/paper/2003.06988/) (2020)
- [Specifying Object Attributes and Relations in Interactive Scene Generation](https://mlsanity.com/paper/1909.05379/) (2019)
- [PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph](https://mlsanity.com/paper/1905.01608/) (2019)
- [House-GAN++: Generative Adversarial Layout Refinement Networks](https://mlsanity.com/paper/2103.02574/) (2021)

## Built on by (most cited first, 8 of 8)

- [Generative AIBIM: An automatic and intelligent structural design pipeline integrating BIM and generative AI](https://mlsanity.com/paper/2311.04052/) (2023)
- [Constrained Layout Generation with Factor Graphs](https://mlsanity.com/paper/2404.00385/) (2024)
- [Computer-Aided Layout Generation for Building Design: A Review](https://mlsanity.com/paper/2504.09694/) (2025)
- [TLC-Plan: A Two-Level Codebook Based Network for End-to-End Vector Floorplan Generation](https://mlsanity.com/paper/2602.07100/) (2026)
- [Rethinking Floorplan Generation with Space Syntax: From Design Theory to Model Improvement](https://mlsanity.com/paper/2602.22507/) (2026)
- [TSC: Topology-Conditioned Stackelberg Coordination for Multi-Agent Reinforcement Learning in Interactive Driving](https://mlsanity.com/paper/2602.23896/) (2026)
- [HypergraphFormer: Learning Hypergraphs from LLMs for Editable Floor Plan Generation](https://mlsanity.com/paper/2605.18932/) (2026)
- [GRE-Diff: Gaussian Room Embeddings for Structured Layout Diffusion](https://mlsanity.com/paper/2607.08086/) (2026)

## Abstract (arXiv)

Layout design is ubiquitous in many applications, e.g. architecture/urban planning, etc, which involves a lengthy iterative design process. Recently, deep learning has been leveraged to automatically generate layouts via image generation, showing a huge potential to free designers from laborious routines. While automatic generation can greatly boost productivity, designer input is undoubtedly crucial. An ideal AI-aided design tool should automate repetitive routines, and meanwhile accept human guidance and provide smart/proactive suggestions. However, the capability of involving humans into the loop has been largely ignored in existing methods which are mostly end-to-end approaches. To this end, we propose a new human-in-the-loop generative model, iPLAN, which is capable of automatically generating layouts, but also interacting with designers throughout the whole procedure, enabling humans and AI to co-evolve a sketchy idea gradually into the final design. iPLAN is evaluated on diverse datasets and compared with existing methods. The results show that iPLAN has high fidelity in producing similar layouts to those from human designers, great flexibility in accepting designer inputs and providing design suggestions accordingly, and strong generalizability when facing unseen design tasks and limited training data.