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MeronymNet: A Hierarchical Approach for Unified and Controllable Multi-Category Object Generation by Baghel, Rishabh; Trivedi, Abhishek; Ravichandran, Tejas; Sarvadevabhatla, Ravi Kiran is a scholarly article available to read on EtoBox.

What is MeronymNet: A Hierarchical Approach for Unified and Controllable Multi-Category Object Generation about?

We introduce MeronymNet, a novel hierarchical approach for controllable, part-based generation of multi-category objects using a single unified model. We adopt a guided coarse-to-fine strategy involving semantically conditioned generation of bounding box layouts, pixel-level part layouts and ultimately, the object depictions themselves. We use Graph Convolutional Networks, Deep Recurrent Networks along with custom-designed Conditional Variational Autoencoders to enable flexible, diverse and category-aware generation of 2-D objects in a controlled manner. The performance scores for generated objects reflect MeronymNet's superior performance compared to multiple strong baselines and ablative variants. We also showcase MeronymNet's suitability for controllable object generation and interactive object editing at various levels of structural and semantic granularity.

Author

Baghel, Rishabh; Trivedi, Abhishek; Ravichandran, Tejas; Sarvadevabhatla, Ravi Kiran

Published

2021

Language

EN