Paper in ECCV 2022 on “BLT: Bidirectional Layout Transformer for Controllable Layout Generation”


Creating visual layouts is a critical step in graphic design. Automatic generation of such layouts is essential for scalable and diverse visual designs. To advance conditional layout generation, we introduce BLT, a bidirectional layout transformer. BLT differs from previous work on transformers in adopting non-autoregressive transformers. In training, BLT learns to predict the masked attributes by attending to surrounding attributes in two directions. During inference, BLT first generates a draft layout from the input and then iteratively refines it into a high-quality layout by masking out low-confident attributes. The masks generated in both training and inference are controlled by a new hierarchical sampling policy. We verify the proposed model on six benchmarks of diverse design tasks. Experimental results demonstrate two benefits compared to the state-of-the-art layout transformer models. First, our model empowers layout transformers to fulfill controllable layout generation. Second, it achieves up to 10x speedup in generating a layout at inference time than the layout transformer baseline. Code is released at

Paper / Citation

Xiang Kong, Lu Jiang, Huiwen Chang, Han Zhang, Yuan Hao, Haifeng Gong, Irfan Essa

BLT: Bidirectional Layout Transformer for Controllable Layout Generation Proceedings Article

In: European Conference on Computer Vision (ECCV), 2022, ISBN: 978-3-031-19789-5.

Abstract | Links | BibTeX | Tags: computer vision, ECCV, generative AI, generative media, google, vision transformer

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