The goal of the PostProdLEAP project is to develop new tools for video archive post-production by
leveraging both recent deep learning approaches and patch-based and variational approaches. Frequent artefacts
observed with deep-learning methods include loss of details, spatial and temporal discontinuities, and
colour bleeding along edges. These artefacts make state-of the-art deep learning approaches inappropriate for
professional post-production. In the framework of the PostProdLEAP project, these limitations will be solved
by designing deep learning models including spatial and temporal regularization and constraints on texture
features. These tools will be developed in collaboration with the artists and historians from Composite Films,
world leader in restoration and colorization of films and archive footage. Through this collaboration, we will
enable artists interaction and give them control on the final results, while making their tasks less time consuming
and tedious than with current professional softwares. Our models will be trained, tested and validated on
datasets of video archives with different resolutions and quality, created specially for the project from movies
restored by Composite Films.
Partners
Aurélie Bugeau, LaBRI, coordinateur
Michaël Clément, LaBRI
Vinh-Thong Ta, LaBRI
Vincent Lepetit, Ecole des Ponts ParisTech
Nicolas Papadakis, IMB, coordinateur local
Jean-François Aujol, IMB
Jérémie Bigot, IMB
Arthur Leclaire, IMB
Yann Traonmilin, IMB
Julie Delon, MAP5, coordinatrice locale
Rémy Alberger, MAP5
Andrés Almansa, MAP5
Agnès Desolneux, CMLA
Bruno Galerne, IDP