Search Results for author:
Found 40 papers, 16 papers with code
Learning Topology Uniformed Face Mesh by Volume Rendering for Multi-view Reconstruction
Our goal is to leverage the superiority of neural volume rendering into multi-view reconstruction of face mesh with consistent topology.
NeRF-HuGS: Improved Neural Radiance Fields in Non-static Scenes Using Heuristics-Guided Segmentation
Neural Radiance Field (NeRF) has been widely recognized for its excellence in novel view synthesis and 3D scene reconstruction.
From NeRFLiX to NeRFLiX++: A General NeRF-Agnostic Restorer Paradigm
To address this, we propose NeRFLiX, a general NeRF-agnostic restorer paradigm that learns a degradation-driven inter-viewpoint mixer.
Ranked #1 on
Novel View Synthesis
on Tanks and Temples
Low-Light Image Enhancement via Structure Modeling and Guidance
Moreover, to improve the appearance modeling, which is implemented with a simple U-Net, a novel structure-guided enhancement module is proposed with structure-guided feature synthesis layers.
NeRFLiX: High-Quality Neural View Synthesis by Learning a Degradation-Driven Inter-viewpoint MiXer
Neural radiance fields (NeRF) show great success in novel view synthesis.
Ranked #1 on
Novel View Synthesis
on LLFF
CRIN: Rotation-Invariant Point Cloud Analysis and Rotation Estimation via Centrifugal Reference Frame
CRIN directly takes the coordinates of points as input and transforms local points into rotation-invariant representations via centrifugal reference frames.
Edge Preserving Implicit Surface Representation of Point Clouds
Among them, the differential Laplican regularizer can effectively alleviate the implicit surface unsmoothness caused by the point cloud quality deteriorates; Meanwhile, in order to reduce the excessive smoothing at the edge regions of implicit suface, we proposed a dynamic edge extract strategy for sampling near the sharp edge of point cloud, which can effectively avoid the Laplacian regularizer from smoothing all regions.
Mutual Guidance and Residual Integration for Image Enhancement
To address those issues, we propose a novel mutual guidance network (MGN) to perform effective bidirectional global-local information exchange while keeping a compact architecture.
Towards Real-World Video Denosing: A Practical Video Denosing Dataset and Network
Moreover, we also propose a new video denoising framework, called Recurrent Video Denoising Transformer (RVDT), which can achieve SOTA performance on PVDD and other current video denoising benchmarks.
Video Frame Interpolation with Transformer
Video frame interpolation (VFI), which aims to synthesize intermediate frames of a video, has made remarkable progress with development of deep convolutional networks over past years.
Ranked #5 on
Video Frame Interpolation
on MSU Video Frame Interpolation
(VMAF metric)
Exploring Motion Ambiguity and Alignment for High-Quality Video Frame Interpolation
Without the bells and whistles, our plug-and-play TCL is capable of improving the performance of existing VFI frameworks.
Ranked #1 on
Video Frame Interpolation
on Middlebury
(PSNR metric)
On Efficient Transformer-Based Image Pre-training for Low-Level Vision
Pre-training has marked numerous state of the arts in high-level computer vision, while few attempts have ever been made to investigate how pre-training acts in image processing systems.
Ranked #5 on
Image Super-Resolution
on Set5 - 2x upscaling
(using extra training data)
Revisiting Temporal Alignment for Video Restoration
Long-range temporal alignment is critical yet challenging for video restoration tasks.
Ranked #1 on
Video Super-Resolution
on Vimeo-90K
Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space
In this paper, we propose a novel design of Sparse Steerable Convolution (SS-Conv) to address the shortcoming; SS-Conv greatly accelerates steerable convolution with sparse tensors, while strictly preserving the property of SE(3)-equivariance.
MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution
Therefore, high-quality correspondence matching is critical.
LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyond
Single image super-resolution (SISR) deals with a fundamental problem of upsampling a low-resolution (LR) image to its high-resolution (HR) version.
Best-Buddy GANs for Highly Detailed Image Super-Resolution
We consider the single image super-resolution (SISR) problem, where a high-resolution (HR) image is generated based on a low-resolution (LR) input.
Video Instance Segmentation with a Propose-Reduce Paradigm
Video instance segmentation (VIS) aims to segment and associate all instances of predefined classes for each frame in videos.
Ranked #2 on
Unsupervised Video Object Segmentation
on DAVIS 2017 (val)
(using extra training data)
Seeing Dynamic Scene in the Dark: A High-Quality Video Dataset With Mechatronic Alignment
Low-light video enhancement is an important task.
Self-Supervised Image Prior Learning With GMM From a Single Noisy Image
It can simultaneously achieve the noise level estimation and the image prior learning directly from only a single noisy image.
JOLO-GCN: Mining Joint-Centered Light-Weight Information for Skeleton-Based Action Recognition
Skeleton-based action recognition has attracted research attentions in recent years.
A deep learning based interactive sketching system for fashion images design
In this work, we propose an interactive system to design diverse high-quality garment images from fashion sketches and the texture information.
MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution
Motivated by these findings, we propose a temporal multi-correspondence aggregation strategy to leverage similar patches across frames, and a cross-scale nonlocal-correspondence aggregation scheme to explore self-similarity of images across scales.
Image Co-skeletonization via Co-segmentation
Object skeletonization in a single natural image is a challenging problem because there is hardly any prior knowledge about the object.
HEMlets PoSh: Learning Part-Centric Heatmap Triplets for 3D Human Pose and Shape Estimation
Estimating 3D human pose from a single image is a challenging task.
HEMlets Pose: Learning Part-Centric Heatmap Triplets for Accurate 3D Human Pose Estimation
Estimating 3D human pose from a single image is a challenging task.
Ranked #1 on
Monocular 3D Human Pose Estimation
on Human3.6M
(Use Video Sequence metric, using extra
training data)
Robust Video Background Identification by Dominant Rigid Motion Estimation
In this paper, we propose an efficient local-to-global method to identify background, based on the assumption that as long as there is sufficient camera motion, the cumulative background features will have the largest amount of trajectories.
Adversarial 3D Human Pose Estimation via Multimodal Depth Supervision
In this paper, a novel deep-learning based framework is proposed to infer 3D human poses from a single image.
FBI-Pose: Towards Bridging the Gap between 2D Images and 3D Human Poses using Forward-or-Backward Information
Although significant advances have been made in the area of human poses estimation from images using deep Convolutional Neural Network (ConvNet), it remains a big challenge to perform 3D pose inference in-the-wild.
Ranked #227 on
3D Human Pose Estimation
on Human3.6M
Locating 3D Object Proposals: A Depth-Based Online Approach
Using efficient but robust registration enables us to combine multiple frames of a scene in near real time and generate 3D bounding boxes for potential 3D regions of interest.
Object Co-Skeletonization With Co-Segmentation
Recent advances in the joint processing of images have certainly shown its advantages over the individual processing.
Direct Photometric Alignment by Mesh Deformation
The choice of motion models is vital in applications like image/video stitching and video stabilization.
Rain Streak Removal Using Layer Priors
This paper addresses the problem of rain streak removal from a single image.
SPM-BP: Sped-up PatchMatch Belief Propagation for Continuous MRFs
However, the quality of the PMBP solution is tightly coupled with the local window size, over which the raw data cost is aggregated to mitigate ambiguity in the data constraint.
Direct Structure Estimation for 3D Reconstruction
Most conventional structure-from-motion (SFM) techniques require camera pose estimation before computing any scene structure.
PISA: Pixelwise Image Saliency by Aggregating Complementary Appearance Contrast Measures with Edge-Preserving Coherence
In this paper, we propose a unified framework called PISA, which stands for Pixelwise Image Saliency Aggregating various bottom-up cues and priors.
Weakly Supervised Fine-Grained Image Categorization
Most existing works heavily rely on object / part detectors to build the correspondence between object parts by using object or object part annotations inside training images.
DAISY Filter Flow: A Generalized Discrete Approach to Dense Correspondences
Fundamental challenges to such an image or scene alignment task are often multifold, which render many existing techniques fall short of producing dense correspondences robustly and efficiently.
PISA: Pixelwise Image Saliency by Aggregating Complementary Appearance Contrast Measures with Spatial Priors
By fusing complementary contrast measures in such a pixelwise adaptive manner, the detection effectiveness is significantly boosted.
Patch Match Filter: Efficient Edge-Aware Filtering Meets Randomized Search for Fast Correspondence Field Estimation
Recent studies on fast cost volume filtering based on efficient edge-aware filters have provided a fast alternative to solve discrete labeling problems, with the complexity independent of the support window size.
