Search Results for author:
Found 25 papers, 15 papers with code
What is Learned in Deep Uncalibrated Photometric Stereo?
Based on this insight, we propose a guided calibration network, named GCNet, that explicitly leverages object shape and shading information for improved lighting estimation.
JIFF: Jointly-aligned Implicit Face Function for High Quality Single View Clothed Human Reconstruction
In this paper, we focus on improving the quality of face in the reconstruction and propose a novel Jointly-aligned Implicit Face Function (JIFF) that combines the merits of the implicit function based approach and model based approach.
A Unified Framework for Masked and Mask-Free Face Recognition via Feature Rectification
We introduce rectification blocks to rectify features extracted by a state-of-the-art recognition model, in both spatial and channel dimensions, to minimize the distance between a masked face and its mask-free counterpart in the rectified feature space.
Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernel
To address the above issues, this paper proposes a model-based blind SISR method under the probabilistic framework, which elaborately models image degradation from the perspectives of noise and blur kernel.
Dense Reconstruction of Transparent Objects by Altering Incident Light Paths Through Refraction
We present a simple setup that allows us to alter the incident light paths before light rays enter the object by immersing the object partially in a liquid, and develop a method for recovering the object surface through reconstructing and triangulating such incident light paths.
HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset
Secondly, we conduct more sophisticated alignment and temporal fusion in the feature space of the coarse HDR video to produce better reconstruction.
Fixed Viewpoint Mirror Surface Reconstruction under an Uncalibrated Camera
This paper addresses the problem of mirror surface reconstruction, and proposes a solution based on observing the reflections of a moving reference plane on the mirror surface.
Learning Spatial Attention for Face Super-Resolution
Visualization of the attention maps shows that our spatial attention network can capture the key face structures well even for very low resolution faces (e. g., $16\times16$).
Progressive Semantic-Aware Style Transformation for Blind Face Restoration
Compared with previous networks, the proposed PSFR-GAN makes full use of the semantic (parsing maps) and pixel (LQ images) space information from different scales of input pairs.
Ranked #3 on
Blind Face Restoration
on CelebA-Test
Face Sketch Synthesis with Style Transfer using Pyramid Column Feature
We utilize a fully convolutional neural network (FCNN) to create the content image, and propose a style transfer approach to introduce textures and shadings based on a newly proposed pyramid column feature.
Deep Photometric Stereo for Non-Lambertian Surfaces
To deal with the uncalibrated scenario where light directions are unknown, we introduce a new convolutional network, named LCNet, to estimate light directions from input images.
Cops-Ref: A new Dataset and Task on Compositional Referring Expression Comprehension
To bridge the gap, we propose a new dataset for visual reasoning in context of referring expression comprehension with two main features.
Look Closer to Ground Better: Weakly-Supervised Temporal Grounding of Sentence in Video
In this paper, we study the problem of weakly-supervised temporal grounding of sentence in video.
Learning Transparent Object Matting
In this paper, we formulate transparent object matting as a refractive flow estimation problem, and propose a deep learning framework, called TOM-Net, for learning the refractive flow.
Weakly-Supervised Spatio-Temporally Grounding Natural Sentence in Video
In this paper, we address a novel task, namely weakly-supervised spatio-temporally grounding natural sentence in video.
Self-calibrating Deep Photometric Stereo Networks
This paper proposes an uncalibrated photometric stereo method for non-Lambertian scenes based on deep learning.
SAFE: Scale Aware Feature Encoder for Scene Text Recognition
We propose a novel scale aware feature encoder (SAFE) that is designed specifically for encoding characters with different scales.
Semi-Supervised Learning for Face Sketch Synthesis in the Wild
Instead of supervising the network with ground truth sketches, we first perform patch matching in feature space between the input photo and photos in a small reference set of photo-sketch pairs.
Ranked #1 on
Face Sketch Synthesis
on CUHK
PS-FCN: A Flexible Learning Framework for Photometric Stereo
This paper addresses the problem of photometric stereo for non-Lambertian surfaces.
Char-Net: A Character-Aware Neural Network for Distorted Scene Text Recognition
Unlike previous work which employed a global spatial transformer network to rectify the entire distorted text image, we take an approach of detecting and rectifying individual characters.
TOM-Net: Learning Transparent Object Matting from a Single Image
In this paper, we first formulate transparent object matting as a refractive flow estimation problem.
SCNet: Learning Semantic Correspondence
This paper addresses the problem of establishing semantic correspondences between images depicting different instances of the same object or scene category.
Mirror Surface Reconstruction Under an Uncalibrated Camera
Unlike previous approaches which require tedious work to calibrate the camera, our method can recover both the camera intrinsics and extrinsics together with the mirror surface from reflections of the reference plane under at least three unknown distinct poses.
A Fixed Viewpoint Approach for Dense Reconstruction of Transparent Objects
In this paper, we develop a fixed viewpoint approach for dense surface reconstruction of transparent objects based on refraction of light.
