Warp and Learn: Novel Views Generation for Vehicles and Other Objects
This work addresses the challenge of realistic 3D view synthesis for applications like autonomous driving and virtual reality, though it is incremental as it builds on existing methods by blending parametric and non-parametric components.
The paper tackles the problem of generating novel views of vehicles and other rigid objects from a single monocular image by introducing a self-supervised, semi-parametric approach that integrates geometric knowledge with deep learning, achieving state-of-the-art results in quantitative and perceptual evaluations.
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 man-made object symmetry and piece-wise planarity to integrate rich a-priori visual information into the novel viewpoint synthesis process. An Image Completion Network (ICN) is then trained to generate a realistic image starting from this geometric guidance. This careful blend between parametric and non-parametric components allows us to i) operate in a real-world scenario, ii) preserve high-frequency visual information such as textures, iii) handle truly arbitrary 3D roto-translations of the input and iv) perform shape transfer to completely different 3D models. Eventually, we show that our approach can be easily complemented with synthetic data and extended to other rigid objects with completely different topology, even in presence of concave structures and holes (e.g. chairs). A comprehensive experimental analysis against state-of-the-art competitors shows the efficacy of our method both from a quantitative and a perceptive point of view. Supplementary material, animated results, code and data are available at: https://github.com/ndrplz/semiparametric