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arXiv · 2006.00190

OPAL-Net: A Generative Model for Part-based Object Layout Generation

Rishabh Baghel·Ravi Kiran Sarvadevabhatla
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Abstract

We propose OPAL-Net, a novel hierarchical architecture for part-based layout generation of objects from multiple categories using a single unified model. We adopt a coarse-to-fine strategy involving semantically conditioned autoregressive generation of bounding box layouts and pixel-level part layouts for objects. We use Graph Convolutional Networks, Deep Recurrent Networks along with custom-designed Conditional Variational Autoencoders to enable flexible, diverse and category-aware generation of object layouts. We train OPAL-Net on PASCAL-Parts dataset. The generated samples and corresponding evaluation scores demonstrate the versatility of OPAL-Net compared to ablative variants and baselines.

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BibTeXRIS

Rishabh Baghel, Ravi Kiran Sarvadevabhatla. 2020-05-30. OPAL-Net: A Generative Model for Part-based Object Layout Generation. https://arxiv.org/abs/2006.00190

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