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Warp and Learn: Novel Views Generation for Vehicles and Other Objects
[article]
2020
arXiv
pre-print
In this work we introduce a new self-supervised, semi-parametric approach for synthesizing novel views of a vehicle starting from a single monocular image. Differently from parametric (i.e. entirely learning-based) methods, we show how a-priori geometric knowledge about the object and the 3D world can be successfully integrated into a deep learning based image generation framework. As this geometric component is not learnt, we call our approach semi-parametric. In particular, we exploit
arXiv:1907.10634v3
fatcat:n4e622lpd5g4bnlo44r54kigpi