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Found 15 papers, 0 papers with code
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.
InverseFaceNet: Deep Monocular Inverse Face Rendering
In contrast, we propose to recover high-quality facial pose, shape, expression, reflectance and illumination using a deep neural network that is trained using a large, synthetically created training corpus.
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.
Video Depth-From-Defocus
Many compelling video post-processing effects, in particular aesthetic focus editing and refocusing effects, are feasible if per-frame depth information is available.
Dense Wide-Baseline Scene Flow From Two Handheld Video Cameras
We finally refine the computed correspondence fields in a variational scene flow formulation.
Partial Sum Minimization of Singular Values in Robust PCA: Algorithm and Applications
Robust Principal Component Analysis (RPCA) via rank minimization is a powerful tool for recovering underlying low-rank structure of clean data corrupted with sparse noise/outliers.
Neural Rendering and Reenactment of Human Actor Videos
In contrast to conventional human character rendering, we do not require the availability of a production-quality photo-realistic 3D model of the human, but instead rely on a video sequence in conjunction with a (medium-quality) controllable 3D template model of the person.
Specular Reflection Separation Using Dark Channel Prior
Our method is based on a novel observation that for most natural images the dark channel can provide an approximate specular-free image.
EgoFace: Egocentric Face Performance Capture and Videorealistic Reenactment
Our lightweight setup allows operations in uncontrolled environments, and lends itself to telepresence applications such as video-conferencing from dynamic environments.
Neural Style-Preserving Visual Dubbing
We present a style-preserving visual dubbing approach from single video inputs, which maintains the signature style of target actors when modifying facial expressions, including mouth motions, to match foreign languages.
Neural Human Video Rendering by Learning Dynamic Textures and Rendering-to-Video Translation
In this paper, we propose a novel human video synthesis method that approaches these limiting factors by explicitly disentangling the learning of time-coherent fine-scale details from the embedding of the human in 2D screen space.
VideoForensicsHQ: Detecting High-quality Manipulated Face Videos
We introduce a new benchmark dataset for face video forgery detection, of unprecedented quality.
HQ3DAvatar: High Quality Controllable 3D Head Avatar
This paper presents a novel approach to building highly photorealistic digital head avatars.
DeepBioisostere: Discovering Bioisosteres with Deep Learning for a Fine Control of Multiple Molecular Properties
Our model's innovation lies in its capacity to design a bioisosteric replacement reflecting the compatibility with the surroundings of the modification site, facilitating the control of sophisticated properties like drug-likeness.
