L'oreal

Elmoznino, Eric

Flament, Frederic Antoinin Raymond Serge

Jiang, Ruowei

Kezele, Irina

Ma, Junwei

Levinshtein, Alex

Despois, Julien

Aarabi, Parham

Ma, He

Perrot, Matthieu

He Ma

Parham Aarabi

Irina Kezele

Alex Levinshtein

Eric Elmoznino

Ruowei Jiang

Matthieu Perrot

Junwei Ma

Julien Despois

Frederic Antoinin Raymond Serge Flament

Edmund Phung

There is shown and described a deep learning based system and method for skin diagnostics as well as testing metrics that show that such a deep learning based system outperforms human experts on the task of apparent skin diagnostics. Also shown and described is a system and method of monitoring a skin treatment regime using a deep learning based system and method for skin diagnostics.

Automatic image-based skin diagnostics using deep learning

Systems and methods relate to a network model to apply an effect to an image such as an augmented reality effect (e.g. makeup, hair, nail, etc.). The network model uses a conditional cycle-consistent generative image-to-image translation model to translate images from a first domain space where the effect is not applied and to a second continuous domain space where the effect is applied. In order to render arbitrary effects (e.g. lipsticks) not seen at training time, the effect's space is represented as a continuous domain (e.g. a conditional variable vector) learned by encoding simple swatch images of the effect, such as are available as product swatches, as well as a null effect. The model is trained end-to-end in an unsupervised fashion. To condition a generator of the model, convolutional conditional batch normalization (CCBN) is used to apply the vector encoding the reference swatch images that represent the makeup properties.

System and method for augmented reality using conditional cycle-consistent generative image-to-image translation models

Growing community of inventors

North York, Canada

He Ma