JoJoGAN: One Shot Face Stylization

Deep Attributes for One-Shot Face Recognition

We address the problem of one-shot unconstrained face recognition. This is addressed by using a deep attribute representation of faces. While face recognition has considered the use of attribute based representations, for one-shot face recognition, the methods proposed so far have been using different features that represent the limited example available. We postulate that by using an intermedi...

Interactive 3D Face Stylization Using Sculptural Abstraction

Sculptors often deviate from geometric accuracy in order to enhance the appearance of their sculpture. These subtle stylizations may emphasize anatomy, draw the viewer’s focus to characteristic features of the subject, or symbolize textures that might not be accurately reproduced in a particular sculptural medium, while still retaining fidelity to the unique proportions of an individual. In thi...

One-shot Face Recognition by Promoting Underrepresented Classes

In this paper, we study the problem of training largescale face identification model with imbalanced training data. This problem naturally exists in many real scenarios including large-scale celebrity recognition, movie actor annotation, etc. Our solution contains two components. First, we build a face feature extraction model, and improve its performance, especially for the persons with very l...

A Shot in the Face

One-Shot Imitation Learning

Imitation learning has been commonly applied to solve different tasks in isolation. This usually requires either careful feature engineering, or a significant number of samples. This is far from what we desire: ideally, robots should be able to learn from very few demonstrations of any given task, and instantly generalize to new situations of the same task, without requiring task-specific engin...