ICCV 2025poster0 citations

Lay-Your-Scene: Natural Scene Layout Generation with Diffusion Transformers

Divyansh Srivastava, Xiang Zhang, He Wen, Chenru Wen, Zhuowen Tu

Abstract

We present Lay-Your-Scene (shorthand LayouSyn), a novel text-to-layout generation pipeline for natural scenes. Prior scene layout generation methods are either closed-vocabulary or use proprietary large language models for open-vocabulary generation, limiting their modeling capabilities and broader applicability in controllable image generation. In this work, we propose to use lightweight open-source language models to obtain scene elements from text prompts and a novel aspect-aware diffusion Transformer architecture trained in an open-vocabulary manner for conditional layout generation. Extensive experiments demonstrate that LayouSyn outperforms existing methods and achieves state-of-the-art performance on challenging spatial and numerical reasoning benchmarks. Additionally, we present two applications of LayouSyn: First, we show that coarse initialization from large language models can be seamlessly combined with our method to achieve better results. Second, we present a pipeline for adding objects to images, demonstrating the potential of LayouSyn in image editing applications.

BibTeX
@InProceedings{Srivastava_2025_ICCV,
    author    = {Srivastava, Divyansh and Zhang, Xiang and Wen, He and Wen, Chenru and Tu, Zhuowen},
    title     = {Lay-Your-Scene: Natural Scene Layout Generation with Diffusion Transformers},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2025},
    pages     = {17909-17919}
}
Lay-Your-Scene: Natural Scene Layout Generation with Diffusion Transformers · ICCV 2025