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Inpainting, which refers to the process of inferring unknown parts of visual content, such as images, videos or surfaces, has witnessed spectacular progresses over the last 20 years, and has been a very fruitful stimulation for the mathematical modeling of such visual content. In this symposium, we aim at bringing together new models and new application fields, as well as stimulating exchanges between different scientific communities. Specifically, the symposium will address sparse inpainting for compression, convolutional neural networks for semantic inpainting, motion-consistent video inpainting, and surfaces interpolation.