Emotion Manipulation for Talking-Head Videos via Facial Landmarks

Manipulating the emotion of a performer in a video is a challenging task. The lip motion needs to be preserved while performing the desired changes in the emotion of the subject; however, simply utilizing existing image-based editing methods sabotages the original lip synchronization. We tackle this problem by utilizing a pretrained StyleGAN paired with a landmark-based editing module that modifies the bias present in the edit direction used in image manipulation. The proposed editing module consists of a latent-based landmark detection network and an editing network that modifies the editing direction to match the original lip synchronization while preserving the desired emotion manipulation results. This is realized by taking the facial landmarks as control points. Both networks operate on the latent space, which enables fast training and inference. We show that the proposed method runs significantly faster and performs better in terms of visual quality than alternative approaches, which was validated through a perceptual study. The proposed method can also be extended to perform face reenactment to generate a talking-head video from a single image and face image manipulation using facial landmarks as control points.