LandmarkGAN: Synthesizing Faces from Landmarks
This work addresses face synthesis for computer vision applications, but appears incremental as it builds on existing GAN-based methods with a specific input representation.
The paper tackles the problem of synthesizing faces from facial landmarks, achieving the ability to generate new faces with consistent expressions and orientations across different subjects.
Face synthesis is an important problem in computer vision with many applications. In this work, we describe a new method, namely LandmarkGAN, to synthesize faces based on facial landmarks as input. Facial landmarks are a natural, intuitive, and effective representation for facial expressions and orientations, which are independent from the target's texture or color and background scene. Our method is able to transform a set of facial landmarks into new faces of different subjects, while retains the same facial expression and orientation. Experimental results on face synthesis and reenactments demonstrate the effectiveness of our method.