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Improving Generative Imagination in Object-Centric World Models

Zhixuan Lin · Yi-Fu Wu · Skand Peri · Bofeng Fu · Jindong Jiang · Sungjin Ahn


Keywords: [ Deep Generative Models ] [ Deep Sequence Models ] [ Representation Learning ] [ Unsupervised Learning ] [ Deep Learning - Generative Models and Autoencoders ]


The remarkable recent advances in object-centric generative world models raise a few questions. First, while many of the recent achievements are indispensable for making a general and versatile world model, it is quite unclear how these ingredients can be integrated into a unified framework. Second, despite using generative objectives, abilities for object detection and tracking are mainly investigated, leaving the crucial ability of temporal imagination largely under question. Third, a few key abilities for more faithful temporal imagination such as multimodal uncertainty and situation-awareness are missing. In this paper, we introduce Generative Structured World Models (G-SWM). The G-SWM achieves the versatile world modeling not only by unifying the key properties of previous models in a principled framework but also by achieving two crucial new abilities, multimodal uncertainty and situation-awareness. Our thorough investigation on the temporal generation ability in comparison to the previous models demonstrates that G-SWM achieves the versatility with the best or comparable performance for all experiment settings including a few complex settings that have not been tested before.

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