1. Future Urban Scenes Generation Through Vehicles Synthesis
- Author
-
Simoni, Alessandro, Bergamini, Luca, Palazzi, Andrea, Calderara, Simone, and Cucchiara, Rita
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Computational Geometry - 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., Comment: Accepted at ICPR2020
- Published
- 2020
- Full Text
- View/download PDF