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Found 18 papers, 0 papers with code
Discrete Diffusion Schrödinger Bridge Matching for Graph Transformation
Our approach extends Iterative Markovian Fitting to discrete domains, and we have proved its convergence to the SB.
GaussianHeads: End-to-End Learning of Drivable Gaussian Head Avatars from Coarse-to-fine Representations
Real-time rendering of human head avatars is a cornerstone of many computer graphics applications, such as augmented reality, video games, and films, to name a few.
PAV: Personalized Head Avatar from Unstructured Video Collection
PAV introduces a method that learns a dynamic deformable neural radiance field (NeRF), in particular from a collection of monocular talking face videos of the same character under various appearance and shape changes.
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.
HQ3DAvatar: High Quality Controllable 3D Head Avatar
This paper presents a novel approach to building highly photorealistic digital head avatars.
VideoForensicsHQ: Detecting High-quality Manipulated Face Videos
We introduce a new benchmark dataset for face video forgery detection, of unprecedented quality.
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.
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.
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 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.
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.
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.
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.
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.
