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Found 38 papers, 21 papers with code
A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning
Our framework consists of three key components, i. e., a parallel contrastive learning scheme for style representation and style transfer, a domain enhancement module for effective learning of style distribution, and a generative network for style transfer.
HairStep: Transfer Synthetic to Real Using Strand and Depth Maps for Single-View 3D Hair Modeling
We firmly think an intermediate representation is essential, but we argue that orientation map using the dominant filtering-based methods is sensitive to uncertain noise and far from a competent representation.
Self-Supervised Category-Level Articulated Object Pose Estimation with Part-Level SE(3) Equivariance
Category-level articulated object pose estimation aims to estimate a hierarchy of articulation-aware object poses of an unseen articulated object from a known category.
LitAR: Visually Coherent Lighting for Mobile Augmented Reality
In this work, we present the design and implementation of a lighting reconstruction framework called LitAR that enables realistic and visually-coherent rendering.
Inversion-Based Style Transfer with Diffusion Models
Our key idea is to learn artistic style directly from a single painting and then guide the synthesis without providing complex textual descriptions.
DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization
Despite the impressive results of arbitrary image-guided style transfer methods, text-driven image stylization has recently been proposed for transferring a natural image into the stylized one according to textual descriptions of the target style provided by the user.
Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning
Our framework consists of three key components, i. e., a multi-layer style projector for style code encoding, a domain enhancement module for effective learning of style distribution, and a generative network for image style transfer.
Towards Self-Supervised Category-Level Object Pose and Size Estimation
Instead, we propose a label-free method that learns to enforce the geometric consistency between category template mesh and observed object point cloud under a self-supervision manner.
Point cloud completion via structured feature maps using a feedback network
To efficiently exploit local structures and enhance point distribution uniformity, we propose IFNet, a point upsampling module with a self-correction mechanism that can progressively refine details of the generated dense point cloud.
Deep Constrained Least Squares for Blind Image Super-Resolution
In this paper, we tackle the problem of blind image super-resolution(SR) with a reformulated degradation model and two novel modules.
Ranked #1 on
Blind Super-Resolution
on DIV2KRK - 2x upscaling
Implicit Neural Deformation for Sparse-View Face Reconstruction
In this work, we present a new method for 3D face reconstruction from sparse-view RGB images.
iShape: A First Step Towards Irregular Shape Instance Segmentation
In this paper, we introduce a brand new dataset to promote the study of instance segmentation for objects with irregular shapes.
Ranked #1 on
Instance Segmentation
on iShape
Scene Synthesis via Uncertainty-Driven Attribute Synchronization
This task is challenging because 3D scenes exhibit diverse patterns, ranging from continuous ones, such as object sizes and the relative poses between pairs of shapes, to discrete patterns, such as occurrence and co-occurrence of objects with symmetrical relationships.
Task-Aware Sampling Layer for Point-Wise Analysis
Sampling, grouping, and aggregation are three important components in the multi-scale analysis of point clouds.
HPNet: Deep Primitive Segmentation Using Hybrid Representations
This paper introduces HPNet, a novel deep-learning approach for segmenting a 3D shape represented as a point cloud into primitive patches.
D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution
Rethinking both, we learn the distribution of underlying high-frequency details in a discrete form and propose a two-stage pipeline: divergence stage to convergence stage.
FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation
Moreover, at the output representation stage, we designed a simple but effective 3D keypoints selection algorithm considering the texture and geometry information of objects, which simplifies keypoint localization for precise pose estimation.
Ranked #1 on
6D Pose Estimation
on LineMOD
NBNet: Noise Basis Learning for Image Denoising with Subspace Projection
Subsequently, image denosing can be achieved by selecting corresponding basis of the signal subspace and projecting the input into such space.
Ranked #1 on
Image Denoising
on SIDD
(SSIM (sRGB) metric)
Practical Deep Raw Image Denoising on Mobile Devices
Deep learning-based image denoising approaches have been extensively studied in recent years, prevailing in many public benchmark datasets.
Arbitrary Video Style Transfer via Multi-Channel Correlation
Towards this end, we propose Multi-Channel Correction network (MCCNet), which can be trained to fuse the exemplar style features and input content features for efficient style transfer while naturally maintaining the coherence of input videos.
FPConv: Learning Local Flattening for Point Convolution
We introduce FPConv, a novel surface-style convolution operator designed for 3D point cloud analysis.
SAM: Squeeze-and-Mimic Networks for Conditional Visual Driving Policy Learning
To learn this representation, we train a squeeze network to drive using annotations for the side task as input.
PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation
Our method is a natural extension of 2D-keypoint approaches that successfully work on RGB based 6DoF estimation.
Ranked #1 on
6D Pose Estimation using RGBD
on YCB-Video
(Mean ADD-S metric)
Disentangled Image Matting
Most previous image matting methods require a roughly-specificed trimap as input, and estimate fractional alpha values for all pixels that are in the unknown region of the trimap.
Semi-supervised Skin Detection by Network with Mutual Guidance
In this paper we present a new data-driven method for robust skin detection from a single human portrait image.
Frame-Recurrent Video Inpainting by Robust Optical Flow Inference
Compared with image inpainting, performing this task on video presents new challenges such as how to preserving temporal consistency and spatial details, as well as how to handle arbitrary input video size and length fast and efficiently.
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation
In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising.
When AWGN-based Denoiser Meets Real Noises
In this paper, we propose a novel approach to boost the performance of a real image denoiser which is trained only with synthetic pixel-independent noise data dominated by AWGN.
Ranked #1 on
Denoising
on Darmstadt Noise Dataset
Global-to-Local Generative Model for 3D Shapes
We also introduce new means to measure and evaluate the performance of an adversarial generative model.
GIF2Video: Color Dequantization and Temporal Interpolation of GIF images
In this paper, we propose GIF2Video, the first learning-based method for enhancing the visual quality of GIFs in the wild.
GeoNet: Deep Geodesic Networks for Point Cloud Analysis
Surface-based geodesic topology provides strong cues for object semantic analysis and geometric modeling.
Gourmet Photography Dataset for Aesthetic Assessment of Food Images
In this study, we present the Gourmet Photography Dataset (GPD), which is the first large-scale dataset for aesthetic assessment of food photographs.
Deep Part Induction from Articulated Object Pairs
In this paper, we explore how the observation of different articulation states provides evidence for part structure and motion of 3D objects.
Video Inpainting by Jointly Learning Temporal Structure and Spatial Details
We present a new data-driven video inpainting method for recovering missing regions of video frames.
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.
High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference
Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a local geometry refinement network.
Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes With Deep Generative Networks
We take an alternative approach: learning a generative model over multi-view depth maps or their corresponding silhouettes, and using a deterministic rendering function to produce 3D shapes from these images.
Learning Local Shape Descriptors from Part Correspondences With Multi-view Convolutional Networks
We present a new local descriptor for 3D shapes, directly applicable to a wide range of shape analysis problems such as point correspondences, semantic segmentation, affordance prediction, and shape-to-scan matching.
