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Semi-parametric Object Synthesis

by   Andrea Palazzi, et al.
University of Modena and Reggio Emilia

We present a new semi-parametric approach to synthesize novel views of an object from a single monocular image. First, 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) then leverages 2.5D sketches rendered from a 3D CAD as guidance to generate a realistic image. In contrast to concurrent works, we do not rely solely on synthetic data but leverage instead existing datasets for 3D object detection to operate in a real-world scenario. Differently from competitors, our semi-parametric framework allows the handling of a wide range of 3D transformations. Thorough experimental analysis against state-of-the-art baselines shows the efficacy of our method both from a quantitative and a perceptive point of view. Code and supplementary material are available at:


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Code Repositories


[TPAMI 2020] Generating Novel Views of Vehicles via Semi-parametric Guidance. A semi-parametric approach for synthesizing novel views of a rigid object from a single monocular image.

view repo