369 research outputs found
ICface: Interpretable and Controllable Face Reenactment Using GANs
This paper presents a generic face animator that is able to control the pose
and expressions of a given face image. The animation is driven by human
interpretable control signals consisting of head pose angles and the Action
Unit (AU) values. The control information can be obtained from multiple sources
including external driving videos and manual controls. Due to the interpretable
nature of the driving signal, one can easily mix the information between
multiple sources (e.g. pose from one image and expression from another) and
apply selective post-production editing. The proposed face animator is
implemented as a two-stage neural network model that is learned in a
self-supervised manner using a large video collection. The proposed
Interpretable and Controllable face reenactment network (ICface) is compared to
the state-of-the-art neural network-based face animation techniques in multiple
tasks. The results indicate that ICface produces better visual quality while
being more versatile than most of the comparison methods. The introduced model
could provide a lightweight and easy to use tool for a multitude of advanced
image and video editing tasks.Comment: Accepted in WACV-202
Text-based Editing of Talking-head Video
Editing talking-head video to change the speech content or to remove filler words is challenging. We propose a novel method to edit talking-head video based on its transcript to produce a realistic output video in which the dialogue of the speaker has been modified, while maintaining a seamless audio-visual flow (i.e. no jump cuts). Our method automatically annotates an input talking-head video with phonemes, visemes, 3D face pose and geometry, reflectance, expression and scene illumination per frame. To edit a video, the user has to only edit the transcript, and an optimization strategy then chooses segments of the input corpus as base material. The annotated parameters corresponding to the selected segments are seamlessly stitched together and used to produce an intermediate video representation in which the lower half of the face is rendered with a parametric face model. Finally, a recurrent video generation network transforms this representation to a photorealistic video that matches the edited transcript. We demonstrate a large variety of edits, such as the addition, removal, and alteration of words, as well as convincing language translation and full sentence synthesis
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