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Found 7 papers, 4 papers with code
Head2HeadFS: Video-based Head Reenactment with Few-shot Learning
Head reenactment is an even more challenging task, which aims at transferring not only the facial expression, but also the entire head pose from a source person to a target.
Head2Head++: Deep Facial Attributes Re-Targeting
Facial video re-targeting is a challenging problem aiming to modify the facial attributes of a target subject in a seamless manner by a driving monocular sequence.
Real-Time Monocular 4D Face Reconstruction using the LSFM models
4D face reconstruction from a single camera is a challenging task, especially when it is required to be performed in real time.
ReenactNet: Real-time Full Head Reenactment
Video-to-video synthesis is a challenging problem aiming at learning a translation function between a sequence of semantic maps and a photo-realistic video depicting the characteristics of a driving video.
Head2Head: Video-based Neural Head Synthesis
In this paper, we propose a novel machine learning architecture for facial reenactment.
DeepFaceFlow: In-the-wild Dense 3D Facial Motion Estimation
Dense 3D facial motion capture from only monocular in-the-wild pairs of RGB images is a highly challenging problem with numerous applications, ranging from facial expression recognition to facial reenactment.
Real-time Facial Expression Recognition "In The Wild'' by Disentangling 3D Expression from Identity
Human emotions analysis has been the focus of many studies, especially in the field of Affective Computing, and is important for many applications, e. g. human-computer intelligent interaction, stress analysis, interactive games, animations, etc.
