Search Results for author:
Found 15 papers, 2 papers with code
Rig3DGS: Creating Controllable Portraits from Casual Monocular Videos
Using a set of control signals, such as head pose and expressions, we transform them to the 3D space with learned deformations to generate the desired rendering.
HeadCraft: Modeling High-Detail Shape Variations for Animated 3DMMs
The decomposition of the parametric model and high-quality vertex displacements allows us to animate the model and modify it semantically.
Controllable Dynamic Appearance for Neural 3D Portraits
The surface normals prediction is guided using 3DMM normals that act as a coarse prior for the normals of the human head, where direct prediction of normals is hard due to rigid and non-rigid deformations induced by head-pose and facial expression changes.
RigNeRF: Fully Controllable Neural 3D Portraits
In this work, we propose RigNeRF, a system that goes beyond just novel view synthesis and enables full control of head pose and facial expressions learned from a single portrait video.
Degraded Reference Image Quality Assessment
In practical media distribution systems, visual content usually undergoes multiple stages of quality degradation along the delivery chain, but the pristine source content is rarely available at most quality monitoring points along the chain to serve as a reference for quality assessment.
Deep Image Debanding
Banding or false contour is an annoying visual artifact whose impact is even more pronounced in ultra high definition, high dynamic range, and wide colour gamut visual content, which is becoming increasingly popular.
FaceDet3D: Facial Expressions with 3D Geometric Detail Hallucination
Facial Expressions induce a variety of high-level details on the 3D face geometry.
FLAME-in-NeRF: Neural control of Radiance Fields for Free View Face Animation
In this work, we design a system that enables 1) novel view synthesis for portrait video, of both the human subject and the scene they are in and 2) explicit control of the facial expressions through a low-dimensional expression representation.
Deep Neural Networks for Blind Image Quality Assessment: Addressing the Data Challenge
This casts great challenges to deep neural network (DNN) based blind IQA (BIQA), which requires large-scale training data that is representative of the natural image distribution.
SIDER: Single-Image Neural Optimization for Facial Geometric Detail Recovery
We present SIDER(Single-Image neural optimization for facial geometric DEtail Recovery), a novel photometric optimization method that recovers detailed facial geometry from a single image in an unsupervised manner.
FLAME-in-NeRF : Neural control of Radiance Fields for Free View Face Animation
In this work, we design a system that enables both novel view synthesis for portrait video, including the human subject and the scene background, and explicit control of the facial expressions through a low-dimensional expression representation.
FaceDet3D: Facial Expressions with 3D Geometric Detail Prediction
The facial details are represented as a vertex displacement map and used then by a Neural Renderer to photo-realistically render novel images of any single image in any desired expression and view.
Variational Feature Disentangling for Fine-Grained Few-Shot Classification
We assume that the distribution of intra-class variance generalizes across the base class and the novel class.
Ranked #14 on
Few-Shot Image Classification
on CUB 200 5-way 5-shot
Self-supervised Deformation Modeling for Facial Expression Editing
In the "motion-editing" step, we explicitly model facial movement through image deformation, warping the image into the desired expression.
Latent Convolutional Models
The learning process provides a latent embedding for every image in the training dataset, as well as a deep convolutional network that maps the latent space to the image space.
