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Found 9 papers, 0 papers with code
Implicit Neural Head Synthesis via Controllable Local Deformation Fields
We build on part-based implicit shape models that decompose a global deformation field into local ones.
Few-shot Geometry-Aware Keypoint Localization
Supervised keypoint localization methods rely on large manually labeled image datasets, where objects can deform, articulate, or occlude.
HQ3DAvatar: High Quality Controllable 3D Head Avatar
This paper presents a novel approach to building highly photorealistic digital head avatars.
Unsupervised Facial Performance Editing via Vector-Quantized StyleGAN Representations
Such representations along with 3D tracking can be used as self-supervision to train a generator with control over coarse expressions and finer facial attributes.
FML: Face Model Learning from Videos
In contrast, we propose multi-frame video-based self-supervised training of a deep network that (i) learns a face identity model both in shape and appearance while (ii) jointly learning to reconstruct 3D faces.
Deep Video Portraits
In order to enable source-to-target video re-animation, we render a synthetic target video with the reconstructed head animation parameters from a source video, and feed it into the trained network -- thus taking full control of the target.
Self-supervised Multi-level Face Model Learning for Monocular Reconstruction at over 250 Hz
To alleviate this problem, we present the first approach that jointly learns 1) a regressor for face shape, expression, reflectance and illumination on the basis of 2) a concurrently learned parametric face model.
MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction
In this work we propose a novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image.
Automatic Face Reenactment
We propose an image-based, facial reenactment system that replaces the face of an actor in an existing target video with the face of a user from a source video, while preserving the original target performance.
