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Look at sample generations by MeronymNet. For each sample, the generated bounding box, corresponding label mask and the RGB object can be seen. Notice the diversity in number of parts, appearance and viewpoint among the generated objects.
Our model allows users to have control on part level, which they can interact with either using boxes or masks. Notice that the viewpoint for rendering the object has changed from the initial generation to accommodate the updated part list. This scenario especially demonstrates MeronymNet’s holistic, part-based awareness of rendering viewpoints best suited for various part sets.
We use the large-scale part-segmented object dataset, PASCAL Parts. The plot shows the density distribution of part counts in object instances for each category. The varying range and frequency of part occurrences across categories, combined with the requirement of object generation from a single unified model, poses lots of challenges.
If you have any question, please contact Dr. Ravi Kiran Sarvadevabhatla at ravi.kiran@iiit.ac.in .