# COFS: Controllable Furniture layout Synthesis

arXiv: https://arxiv.org/abs/2205.14657 · HTML: https://mlsanity.com/paper/2205.14657/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 42 references that are in the map.
- First posted on arXiv 29 May 2022.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention](https://mlsanity.com/paper/2006.16236/) (2020)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [MaskGIT: Masked Generative Image Transformer](https://mlsanity.com/paper/2202.04200/) (2022)
- [3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics](https://mlsanity.com/paper/2011.09127/) (2020)
- [One weird trick for parallelizing convolutional neural networks](https://mlsanity.com/paper/1404.5997/) (2014)
- [ATISS: Autoregressive Transformers for Indoor Scene Synthesis](https://mlsanity.com/paper/2110.03675/) (2021)
- [BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model](https://mlsanity.com/paper/1902.04094/) (2019)
- [SceneFormer: Indoor Scene Generation with Transformers](https://mlsanity.com/paper/2012.09793/) (2020)
- [Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models](https://mlsanity.com/paper/1811.12463/) (2018)
- [Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds](https://mlsanity.com/paper/2203.10314/) (2022)
- [BLT: Bidirectional Layout Transformer for Controllable Layout Generation](https://mlsanity.com/paper/2112.05112/) (2021)
- [Generative Layout Modeling using Constraint Graphs](https://mlsanity.com/paper/2011.13417/) (2020)
- [A Generalized Framework of Sequence Generation with Application to Undirected Sequence Models](https://mlsanity.com/paper/1905.12790/) (2019)
- [Fast and Scalable Position-Based Layout Synthesis](https://mlsanity.com/paper/1809.10526/) (2018)
- [Deep Permutation Equivariant Structure from Motion](https://mlsanity.com/paper/2104.06703/) (2021)
- [EdiBERT, a generative model for image editing](https://mlsanity.com/paper/2111.15264/) (2021)
- [Permutation-Invariant Relational Network for Multi-person 3D Pose Estimation](https://mlsanity.com/paper/2204.04913/) (2022)

## Built on by (most cited first, 14 of 14)

- [MIME: Human-Aware 3D Scene Generation](https://mlsanity.com/paper/2212.04360/) (2022)
- [HSM: Hierarchical Scene Motifs for Multi-Scale Indoor Scene Generation](https://mlsanity.com/paper/2503.16848/) (2025)
- [3D Scene Generation: A Survey](https://mlsanity.com/paper/2505.05474/) (2025)
- [DeBaRA: Denoising-Based 3D Room Arrangement Generation](https://mlsanity.com/paper/2409.18336/) (2024)
- [CasaGPT: Cuboid Arrangement and Scene Assembly for Interior Design](https://mlsanity.com/paper/2504.19478/) (2025)
- [Forest2Seq: Revitalizing Order Prior for Sequential Indoor Scene Synthesis](https://mlsanity.com/paper/2407.05388/) (2024)
- [SemLayoutDiff: Semantic Layout Generation with Diffusion Model for Indoor Scene Synthesis](https://mlsanity.com/paper/2508.18597/) (2025)
- [BOOTPLACE: Bootstrapped Object Placement with Detection Transformers](https://mlsanity.com/paper/2503.21991/) (2025)
- [Computer-Aided Layout Generation for Building Design: A Review](https://mlsanity.com/paper/2504.09694/) (2025)
- [Prim2Room: Layout-Controllable Room Mesh Generation from Primitives](https://mlsanity.com/paper/2409.05380/) (2024)
- [SceneNAT: Masked Generative Modeling for Language-Guided Indoor Scene Synthesis](https://mlsanity.com/paper/2601.07218/) (2026)
- [HiGS: Hierarchical Generative Scene Framework for Multi-Step Associative Semantic Spatial Composition](https://mlsanity.com/paper/2510.27148/) (2025)
- [HOG-Layout: Hierarchical 3D Scene Generation, Optimization and Editing via Vision-Language Models](https://mlsanity.com/paper/2604.10772/) (2026)
- [CasLayout: Cascaded 3D Layout Diffusion for Indoor Scene Synthesis with Implicit Relation Modeling](https://mlsanity.com/paper/2604.27361/) (2026)

## Abstract (arXiv)

Scalable generation of furniture layouts is essential for many applications in virtual reality, augmented reality, game development and synthetic data generation. Many existing methods tackle this problem as a sequence generation problem which imposes a specific ordering on the elements of the layout making such methods impractical for interactive editing or scene completion. Additionally, most methods focus on generating layouts unconditionally and offer minimal control over the generated layouts. We propose COFS, an architecture based on standard transformer architecture blocks from language modeling. The proposed model is invariant to object order by design, removing the unnatural requirement of specifying an object generation order. Furthermore, the model allows for user interaction at multiple levels enabling fine grained control over the generation process. Our model consistently outperforms other methods which we verify by performing quantitative evaluations. Our method is also faster to train and sample from, compared to existing methods.