Generating unseen complex scenes: are we there yet?

Understand

Although recent complex scene conditional generation models generate increasingly appealing scenes, it is very hard to assess which models perform better and why.

• This is often due to models being trained to fit different data splits, and defining their own experimental setups.
• In this paper, we propose a methodology to compare complex scene conditional generation models, and provide an in-depth analysis that assesses the ability of each model to (1) fit the training distribution and hence perform well on seen conditionings, (2) to generalize to unseen conditionings composed of seen object combinations, and (3) generalize to unseen conditionings composed of unseen object combinations.
• As a result, we observe that recent methods are able to generate recognizable scenes given seen conditionings, and exploit compositionality to generalize to unseen conditionings with seen object combinations.