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arXiv · 2004.01071

Model-based occlusion disentanglement for image-to-image translation

Fabio Pizzati·Pietro Cerri·Raoul de Charette
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Abstract

Image-to-image translation is affected by entanglement phenomena, which may occur in case of target data encompassing occlusions such as raindrops, dirt, etc. Our unsupervised model-based learning disentangles scene and occlusions, while benefiting from an adversarial pipeline to regress physical parameters of the occlusion model. The experiments demonstrate our method is able to handle varying types of occlusions and generate highly realistic translations, qualitatively and quantitatively outperforming the state-of-the-art on multiple datasets.

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Fabio Pizzati, Pietro Cerri, Raoul de Charette. 2020-04-02. Model-based occlusion disentanglement for image-to-image translation. https://arxiv.org/abs/2004.01071

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