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Found 17 papers, 9 papers with code
3D Facial Expressions through Analysis-by-Neural-Synthesis
Instead, SMIRK replaces the differentiable rendering with a neural rendering module that, given the rendered predicted mesh geometry, and sparsely sampled pixels of the input image, generates a face image.
Neural Text to Articulate Talk: Deep Text to Audiovisual Speech Synthesis achieving both Auditory and Photo-realism
Our method, which we call NEUral Text to ARticulate Talk (NEUTART), is a talking face generator that uses a joint audiovisual feature space, as well as speech-informed 3D facial reconstructions and a lip-reading loss for visual supervision.
3D Neural Sculpting (3DNS): Editing Neural Signed Distance Functions
In recent years, implicit surface representations through neural networks that encode the signed distance have gained popularity and have achieved state-of-the-art results in various tasks (e. g. shape representation, shape reconstruction, and learning shape priors).
Neural Sign Reenactor: Deep Photorealistic Sign Language Retargeting
In this paper, we introduce a neural rendering pipeline for transferring the facial expressions, head pose, and body movements of one person in a source video to another in a target video.
Visual Speech-Aware Perceptual 3D Facial Expression Reconstruction from Videos
The recent state of the art on monocular 3D face reconstruction from image data has made some impressive advancements, thanks to the advent of Deep Learning.
Neural Emotion Director: Speech-preserving semantic control of facial expressions in "in-the-wild" videos
In this paper, we introduce a novel deep learning method for photo-realistic manipulation of the emotional state of actors in "in-the-wild" videos.
Deep Semantic Manipulation of Facial Videos
We focus on interactive manipulation of the videos by altering and controlling the facial expressions, achieving promising photorealistic results.
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.
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.
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.
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.
Estimating Correspondences of Deformable Objects "In-The-Wild"
In this paper, we show for the first time, to the best of our knowledge, that it is possible to construct SDMs by putting object shapes in dense correspondence.
A 3D Morphable Model Learnt From 10,000 Faces
We present Large Scale Facial Model (LSFM) -- a 3D Morphable Model (3DMM) automatically constructed from 9, 663 distinct facial identities.
Face Flow
In this paper, we propose a method for the robust and efficient computation of multi-frame optical flow in an expressive sequence of facial images.
Dense Variational Reconstruction of Non-rigid Surfaces from Monocular Video
This paper offers the first variational approach to the problem of dense 3D reconstruction of non-rigid surfaces from a monocular video sequence.
