Future Urban Scenes Generation Through Vehicles Synthesis

• URL: http://arxiv.org/abs/2007.00323v3
• Date: Fri, 22 Oct 2021 07:54:00 GMT
• Title: Future Urban Scenes Generation Through Vehicles Synthesis
• Authors: Alessandro Simoni and Luca Bergamini and Andrea Palazzi and Simone Calderara and Rita Cucchiara
• Abstract summary: We propose a deep learning pipeline to predict the visual future appearance of an urban scene. We follow a two stages approach, where interpretable information is included in the loop and each actor is modelled independently. We show the superiority of this approach over traditional end-to-end scene-generation methods on CityFlow.
• Score: 90.1731992199415
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In this work we propose a deep learning pipeline to predict the visual future appearance of an urban scene. Despite recent advances, generating the entire scene in an end-to-end fashion is still far from being achieved. Instead, here we follow a two stages approach, where interpretable information is included in the loop and each actor is modelled independently. We leverage a per-object novel view synthesis paradigm; i.e. generating a synthetic representation of an object undergoing a geometrical roto-translation in the 3D space. Our model can be easily conditioned with constraints (e.g. input trajectories) provided by state-of-the-art tracking methods or by the user itself. This allows us to generate a set of diverse realistic futures starting from the same input in a multi-modal fashion. We visually and quantitatively show the superiority of this approach over traditional end-to-end scene-generation methods on CityFlow, a challenging real world dataset.

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