209 citations · 588 across the 38 of their papers we have counts for
39 papers
3D Keypoint Estimation Using Implicit Representation Learning
Xiangyu Zhu, Dong Du, Haibin Huang +2
In this paper, we tackle the challenging problem of 3D keypoint estimation of general objects using a novel implicit representation. Previous works have demonstrated promising resu…
Multi-Modal Face Stylization with a Generative Prior
Mengtian Li, Yi Dong, Minxuan Lin +3
In this work, we introduce a new approach for face stylization. Despite existing methods achieving impressive results in this task, there is still room for improvement in generatin…
ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models
Yuxin Zhang, Weiming Dong, Fan Tang +6
Personalizing generative models offers a way to guide image generation with user-provided references. Current personalization methods can invert an object or concept into the textu…
Semi-Weakly Supervised Object Kinematic Motion Prediction
Gengxin Liu, Qian Sun, Haibin Huang +5
Given a 3D object, kinematic motion prediction aims to identify the mobile parts as well as the corresponding motion parameters. Due to the large variations in both topological str…
HairStep: Transfer Synthetic to Real Using Strand and Depth Maps for Single-View 3D Hair Modeling
Yujian Zheng, Zirong Jin, Moran Li +4
In this work, we tackle the challenging problem of learning-based single-view 3D hair modeling. Due to the great difficulty of collecting paired real image and 3D hair data, using…
A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning
Yuxin Zhang, Fan Tang, Weiming Dong +4
We present Unified Contrastive Arbitrary Style Transfer (UCAST), a novel style representation learning and transfer framework, which can fit in most existing arbitrary image style…
Self-Supervised Category-Level Articulated Object Pose Estimation with Part-Level SE(3) Equivariance
Xueyi Liu, Ji Zhang, Ruizhen Hu +3
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. To reduce t…
LitAR: Visually Coherent Lighting for Mobile Augmented Reality
Yiqin Zhao, Chongyang Ma, Haibin Huang +1
An accurate understanding of omnidirectional environment lighting is crucial for high-quality virtual object rendering in mobile augmented reality (AR). In particular, to support r…
Inversion-Based Style Transfer with Diffusion Models
Yuxin Zhang, Nisha Huang, Fan Tang +4
The artistic style within a painting is the means of expression, which includes not only the painting material, colors, and brushstrokes, but also the high-level attributes includi…
DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization
Nisha Huang, Yuxin Zhang, Fan Tang +5
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 a st…
Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning
Yuxin Zhang, Fan Tang, Weiming Dong +4
In this work, we tackle the challenging problem of arbitrary image style transfer using a novel style feature representation learning method. A suitable style representation, as a…
Towards Self-Supervised Category-Level Object Pose and Size Estimation
Yisheng He, Haoqiang Fan, Haibin Huang +2
In this work, we tackle the challenging problem of category-level object pose and size estimation from a single depth image. Although previous fully-supervised works have demonstra…
Deep Constrained Least Squares for Blind Image Super-Resolution
Ziwei Luo, Haibin Huang, Lei Yu +3
In this paper, we tackle the problem of blind image super-resolution(SR) with a reformulated degradation model and two novel modules. Following the common practices of blind SR, ou…
Point cloud completion via structured feature maps using a feedback network
Zejia Su, Haibin Huang, Chongyang Ma +2
In this paper, we tackle the challenging problem of point cloud completion from the perspective of feature learning. Our key observation is that to recover the underlying structure…
Implicit Neural Deformation for Sparse-View Face Reconstruction
Moran Li, Haibin Huang, Yi Zheng +3
In this work, we present a new method for 3D face reconstruction from sparse-view RGB images. Unlike previous methods which are built upon 3D morphable models (3DMMs) with limited…
iShape: A First Step Towards Irregular Shape Instance Segmentation
Lei Yang, Yan Zi Wei, Yisheng HE +4
In this paper, we introduce a brand new dataset to promote the study of instance segmentation for objects with irregular shapes. Our key observation is that though irregularly shap…
Scene Synthesis via Uncertainty-Driven Attribute Synchronization
Haitao Yang, Zaiwei Zhang, Siming Yan +5
Developing deep neural networks to generate 3D scenes is a fundamental problem in neural synthesis with immediate applications in architectural CAD, computer graphics, as well as i…
Task-Aware Sampling Layer for Point-Wise Analysis
Yiqun Lin, Lichang Chen, Haibin Huang +3
Sampling, grouping, and aggregation are three important components in the multi-scale analysis of point clouds. In this paper, we present a novel data-driven sampler learning strat…
HPNet: Deep Primitive Segmentation Using Hybrid Representations
Siming Yan, Zhenpei Yang, Chongyang Ma +3
This paper introduces HPNet, a novel deep-learning approach for segmenting a 3D shape represented as a point cloud into primitive patches. The key to deep primitive segmentation is…
FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation
Yisheng He, Haibin Huang, Haoqiang Fan +2
In this work, we present FFB6D, a Full Flow Bidirectional fusion network designed for 6D pose estimation from a single RGBD image. Our key insight is that appearance information in…
D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution
Youwei Li, Haibin Huang, Lanpeng Jia +2
In this paper, we present D2C-SR, a novel framework for the task of real-world image super-resolution. As an ill-posed problem, the key challenge in super-resolution related tasks…
NBNet: Noise Basis Learning for Image Denoising with Subspace Projection
Shen Cheng, Yuzhi Wang, Haibin Huang +3
In this paper, we introduce NBNet, a novel framework for image denoising. Unlike previous works, we propose to tackle this challenging problem from a new perspective: noise reducti…
Practical Deep Raw Image Denoising on Mobile Devices
Yuzhi Wang, Haibin Huang, Qin Xu +3
Deep learning-based image denoising approaches have been extensively studied in recent years, prevailing in many public benchmark datasets. However, the stat-of-the-art networks ar…
Arbitrary Video Style Transfer via Multi-Channel Correlation
Yingying Deng, Fan Tang, Weiming Dong +3
Video style transfer is getting more attention in AI community for its numerous applications such as augmented reality and animation productions. Compared with traditional image st…