Search Results for author:
Found 16 papers, 12 papers with code
VOODOO 3D: Volumetric Portrait Disentanglement for One-Shot 3D Head Reenactment
We present a 3D-aware one-shot head reenactment method based on a fully volumetric neural disentanglement framework for source appearance and driver expressions.
QC-StyleGAN -- Quality Controllable Image Generation and Manipulation
The network can synthesize various image degradation and restore the sharp image via a quality control code.
Self-Supervised Learning with Multi-View Rendering for 3D Point Cloud Analysis
Inspired by the success of deep learning in the image domain, we devise a novel pre-training technique for better model initialization by utilizing the multi-view rendering of the 3D data.
Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey
The availability of representative datasets is an essential prerequisite for many successful artificial intelligence and machine learning models.
HyperInverter: Improving StyleGAN Inversion via Hypernetwork
In the first phase, we train an encoder to map the input image to StyleGAN2 $\mathcal{W}$-space, which was proven to have excellent editability but lower reconstruction quality.
Exploiting Domain-Specific Features to Enhance Domain Generalization
We empirically show that mDSDI provides competitive results with state-of-the-art techniques in DG.
LASSO: Latent Sub-spaces Orientation for Domain Generalization
To achieve a satisfactory generalization performance on prediction tasks in an unseen domain, existing domain generalization (DG) approaches often rely on the strict assumption of fixed domain-invariant features and common hypotheses learned from a set of training domains.
Toward Realistic Single-View 3D Object Reconstruction with Unsupervised Learning from Multiple Images
In this paper, we eliminate the symmetry requirement with a novel unsupervised algorithm that can learn a 3D reconstruction network from a multi-image dataset.
Explore Image Deblurring via Encoded Blur Kernel Space
This paper introduces a method to encode the blur operators of an arbitrary dataset of sharp-blur image pairs into a blur kernel space.
WaNet - Imperceptible Warping-based Backdoor Attack
With the thriving of deep learning and the widespread practice of using pre-trained networks, backdoor attacks have become an increasing security threat drawing many research interests in recent years.
ExpNet: Landmark-Free, Deep, 3D Facial Expressions
Our ExpNet CNN is applied directly to the intensities of a face image and regresses a 29D vector of 3D expression coefficients.
Ranked #1 on
3D Facial Expression Recognition
on 2017_test set
(using extra training data)
Extreme 3D Face Reconstruction: Seeing Through Occlusions
Motivated by the concept of bump mapping, we propose a layered approach which decouples estimation of a global shape from its mid-level details (e. g., wrinkles).
FacePoseNet: Making a Case for Landmark-Free Face Alignment
Instead, we compare our FPN with existing methods by evaluating how they affect face recognition accuracy on the IJB-A and IJB-B benchmarks: using the same recognition pipeline, but varying the face alignment method.
Ranked #1 on
Facial Landmark Detection
on 300W
(Mean Error Rate metric)
On Face Segmentation, Face Swapping, and Face Perception
To this end, we use the Labeled Faces in the Wild (LFW) benchmark and measure the effect of intra- and inter-subject face swapping on recognition.
Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network
The 3D shapes of faces are well known to be discriminative.
Ranked #4 on
3D Face Reconstruction
on Florence
(Average 3D Error metric)
Do We Really Need to Collect Millions of Faces for Effective Face Recognition?
Face recognition capabilities have recently made extraordinary leaps.
Ranked #12 on
Face Verification
on IJB-A
