134 citations · 513 across the 41 of their papers we have counts for
26 papers · 1 filter
SketchMetaFace: A Learning-based Sketching Interface for High-fidelity 3D Character Face Modeling
Zhongjin Luo, Dong Du, Heming Zhu +3
Modeling 3D avatars benefits various application scenarios such as AR/VR, gaming, and filming. Character faces contribute significant diversity and vividity as a vital component of…
3DPortraitGAN: Learning One-Quarter Headshot 3D GANs from a Single-View Portrait Dataset with Diverse Body Poses
Yiqian Wu, Hao Xu, Xiangjun Tang +3
3D-aware face generators are typically trained on 2D real-life face image datasets that primarily consist of near-frontal face data, and as such, they are unable to construct one-q…
Sketch2Stress: Sketching with Structural Stress Awareness
Deng Yu, Chufeng Xiao, Manfred Lau +1
In the process of product design and digital fabrication, the structural analysis of a designed prototype is a fundamental and essential step. However, such a step is usually invis…
Sketch Beautification: Learning Part Beautification and Structure Refinement for Sketches of Man-made Objects
Deng Yu, Manfred Lau, Lin Gao +1
We present a novel freehand sketch beautification method, which takes as input a freely drawn sketch of a man-made object and automatically beautifies it both geometrically and str…
LPFF: A Portrait Dataset for Face Generators Across Large Poses
Yiqian Wu, Jing Zhang, Hongbo Fu +1
The creation of 2D realistic facial images and 3D face shapes using generative networks has been a hot topic in recent years. Existing face generators exhibit exceptional performan…
LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation
Zeyu Hu, Xuyang Bai, Runze Zhang +4
We propose LiDAL, a novel active learning method for 3D LiDAR semantic segmentation by exploiting inter-frame uncertainty among LiDAR frames. Our core idea is that a well-trained m…
MonoNeuralFusion: Online Monocular Neural 3D Reconstruction with Geometric Priors
Zi-Xin Zou, Shi-Sheng Huang, Yan-Pei Cao +3
High-fidelity 3D scene reconstruction from monocular videos continues to be challenging, especially for complete and fine-grained geometry reconstruction. The previous 3D reconstru…
DifferSketching: How Differently Do People Sketch 3D Objects?
Chufeng Xiao, Wanchao Su, Jing Liao +3
Multiple sketch datasets have been proposed to understand how people draw 3D objects. However, such datasets are often of small scale and cover a small set of objects or categories…
DeepPortraitDrawing: Generating Human Body Images from Freehand Sketches
Xian Wu, Chen Wang, Hongbo Fu +3
Researchers have explored various ways to generate realistic images from freehand sketches, e.g., for objects and human faces. However, how to generate realistic human body images…
TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers
Xuyang Bai, Zeyu Hu, Xinge Zhu +4
LiDAR and camera are two important sensors for 3D object detection in autonomous driving. Despite the increasing popularity of sensor fusion in this field, the robustness against i…
NeuralReshaper: Single-image Human-body Retouching with Deep Neural Networks
Beijia Chen, Yuefan Shen, Hongbo Fu +3
In this paper, we present NeuralReshaper, a novel method for semantic reshaping of human bodies in single images using deep generative networks. To achieve globally coherent reshap…
SketchHairSalon: Deep Sketch-based Hair Image Synthesis
Chufeng Xiao, Deng Yu, Xiaoguang Han +2
Recent deep generative models allow real-time generation of hair images from sketch inputs. Existing solutions often require a user-provided binary mask to specify a target hair sh…
SimpModeling: Sketching Implicit Field to Guide Mesh Modeling for 3D Animalmorphic Head Design
Zhongjin Luo, Jie Zhou, Heming Zhu +3
Head shapes play an important role in 3D character design. In this work, we propose SimpModeling, a novel sketch-based system for helping users, especially amateur users, easily mo…
WSDesc: Weakly Supervised 3D Local Descriptor Learning for Point Cloud Registration
Lei Li, Hongbo Fu, Maks Ovsjanikov
In this work, we present a novel method called WSDesc to learn 3D local descriptors in a weakly supervised manner for robust point cloud registration. Our work builds upon recent 3…
VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation
Zeyu Hu, Xuyang Bai, Jiaxiang Shang +6
In recent years, sparse voxel-based methods have become the state-of-the-arts for 3D semantic segmentation of indoor scenes, thanks to the powerful 3D CNNs. Nevertheless, being obl…
PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency
Xuyang Bai, Zixin Luo, Lei Zhou +5
Removing outlier correspondences is one of the critical steps for successful feature-based point cloud registration. Despite the increasing popularity of introducing deep learning…
Scale-aware Insertion of Virtual Objects in Monocular Videos
Songhai Zhang, Xiangli Li, Yingtian Liu +1
In this paper, we propose a scale-aware method for inserting virtual objects with proper sizes into monocular videos. To tackle the scale ambiguity problem of geometry recovery fro…
JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds
Zeyu Hu, Mingmin Zhen, Xuyang Bai +2
Semantic segmentation and semantic edge detection can be seen as two dual problems with close relationships in computer vision. Despite the fast evolution of learning-based 3D sema…
End-to-End Learning Local Multi-view Descriptors for 3D Point Clouds
Lei Li, Siyu Zhu, Hongbo Fu +2
In this work, we propose an end-to-end framework to learn local multi-view descriptors for 3D point clouds. To adopt a similar multi-view representation, existing studies use hand-…
D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
Xuyang Bai, Zixin Luo, Lei Zhou +3
A successful point cloud registration often lies on robust establishment of sparse matches through discriminative 3D local features. Despite the fast evolution of learning-based 3D…
SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks
Lumin Yang, Jiajie Zhuang, Hongbo Fu +3
We introduce SketchGNN, a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches. We treat an input stroke-based sketch as a graph, w…
SketchDesc: Learning Local Sketch Descriptors for Multi-view Correspondence
Deng Yu, Lei Li, Youyi Zheng +4
In this paper, we study the problem of multi-view sketch correspondence, where we take as input multiple freehand sketches with different views of the same object and predict as ou…
Sketch-R2CNN: An Attentive Network for Vector Sketch Recognition
Lei Li, Changqing Zou, Youyi Zheng +3
Freehand sketching is a dynamic process where points are sequentially sampled and grouped as strokes for sketch acquisition on electronic devices. To recognize a sketched object, m…
LUCSS: Language-based User-customized Colourization of Scene Sketches
Changqing Zou, Haoran Mo, Ruofei Du +3
We introduce LUCSS, a language-based system for interactive col- orization of scene sketches, based on their semantic understanding. LUCSS is built upon deep neural networks traine…