# ActFloor-GAN: Activity-Guided Adversarial Networks for Human-Centric Floorplan Design

arXiv: https://arxiv.org/abs/2111.03545 · HTML: https://mlsanity.com/paper/2111.03545/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2021.

## Built on by (most cited first, 12 of 12)

- [Deep Learning for Omnidirectional Vision: A Survey and New Perspectives](https://mlsanity.com/paper/2205.10468/) (2022)
- [Generative AI Models for Different Steps in Architectural Design: A Literature Review](https://mlsanity.com/paper/2404.01335/) (2024)
- [Text2BIM: Generating Building Models Using a Large Language Model-based Multi-Agent Framework](https://mlsanity.com/paper/2408.08054/) (2024)
- [Generating 3D House Wireframes with Semantics](https://mlsanity.com/paper/2407.12267/) (2024)
- [ReCo: A Dataset for Residential Community Layout Planning](https://mlsanity.com/paper/2206.04678/) (2022)
- [LLM4DESIGN: An Automated Multi-Modal System for Architectural and Environmental Design](https://mlsanity.com/paper/2407.12025/) (2024)
- [Unit Region Encoding: A Unified and Compact Geometry-aware Representation for Floorplan Applications](https://mlsanity.com/paper/2501.11097/) (2025)
- [A Framework of Full-Process Generation Design for Park Green Spaces Based on Remote Sensing Segmentation-GAN-Diffusion](https://mlsanity.com/paper/2312.10674/) (2023)
- [Automated architectural space layout planning using a physics-inspired generative design framework](https://mlsanity.com/paper/2406.14840/) (2024)
- [BuildingBRep-11K: Precise Multi-Storey B-Rep Building Solids with Rich Layout Metadata](https://mlsanity.com/paper/2506.15718/) (2025)
- [GFLAN: Generative Functional Layouts](https://mlsanity.com/paper/2512.16275/) (2025)
- [TLC-Plan: A Two-Level Codebook Based Network for End-to-End Vector Floorplan Generation](https://mlsanity.com/paper/2602.07100/) (2026)

## Abstract (arXiv)

We present a novel two-stage approach for automated floorplan design in residential buildings with a given exterior wall boundary. Our approach has the unique advantage of being human-centric, that is, the generated floorplans can be geometrically plausible, as well as topologically reasonable to enhance resident interaction with the environment. From the input boundary, we first synthesize a human-activity map that reflects both the spatial configuration and human-environment interaction in an architectural space. We propose to produce the human-activity map either automatically by a pre-trained generative adversarial network (GAN) model, or semi-automatically by synthesizing it with user manipulation of the furniture. Second, we feed the human-activity map into our deep framework ActFloor-GAN to guide a pixel-wise prediction of room types. We adopt a re-formulated cycle-consistency constraint in ActFloor-GAN to maximize the overall prediction performance, so that we can produce high-quality room layouts that are readily convertible to vectorized floorplans. Experimental results show several benefits of our approach. First, a quantitative comparison with prior methods shows superior performance of leveraging the human-activity map in predicting piecewise room types. Second, a subjective evaluation by architects shows that our results have compelling quality as professionally-designed floorplans and much better than those generated by existing methods in terms of the room layout topology. Last, our approach allows manipulating the furniture placement, considers the human activities in the environment, and enables the incorporation of user-design preferences.