L'oreal

Zeqi Li

Parham Aarabi

Ruowei Jiang

There is provided methods, devices and techniques to process an image using a deep learning model to achieve continuous effect simulation by a unified network where a simple (effect class) estimator is embedded into a regular encoder-decoder architecture. The estimator allows learning of model-estimated class embeddings of all effect classes (e.g. progressive degrees of the effect), thus representing the continuous effect information without manual efforts in selecting proper anchor effect groups. In an embodiment, given a target age class, there is derived a personalized age embedding which considers two aspects of face aging: 1) a personalized residual age embedding at a model-estimated age of the subject, preserving the subject's aging information; and 2) exemplar-face aging basis at the target age, encoding the shared aging patterns among the entire population. Training and runtime (inference time) embodiments are described including an AR application that generates recommendations and provides ecommerce services.

Applying a continuous effect via model-estimated class embeddings

GANs based generators are useful to perform image to image translations. GANs models have large storage sizes and resource use requirements such that they are too large to be deployed directly on mobile devices. Systems and methods define through conditioning a student GANs model having a student generator that is scaled downwardly from a teacher GANs model (and generator) using knowledge distillation. A semantic relation knowledge distillation loss is used to transfer semantic knowledge from an intermediate layer of the teacher to an intermediate layer of the student. Student generators thus defined are stored and executed by mobile devices such as smartphones and laptops to provide augmented reality experiences. Effects are simulated on images, including makeup, hair, nail and age simulation effects.

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Toronto, Canada

Zeqi Li