65 citations · 252 across the 21 of their papers we have counts for
21 papers
HairLRM: Strand-based Hair Modeling via Large Reconstruction Models
Yuefan Shen, Yican Dong, Xiufeng Huang +3
The fundamental limitation of traditional strand-based modeling is not simply data scarcity, but the ill-posedness of inferring complex 3D fields from 2D imagery without structural…
VDN-NeRF: Resolving Shape-Radiance Ambiguity via View-Dependence Normalization
Bingfan Zhu, Yanchao Yang, Xulong Wang +2
We propose VDN-NeRF, a method to train neural radiance fields (NeRFs) for better geometry under non-Lambertian surface and dynamic lighting conditions that cause significant variat…
OrthoGAN:High-Precision Image Generation for Teeth Orthodontic Visualization
Feihong Shen, JIngjing Liu, Jianwen Lou +6
Patients take care of what their teeth will be like after the orthodontics. Orthodontists usually describe the expectation movement based on the original smile images, which is unc…
NeuralHDHair: Automatic High-fidelity Hair Modeling from a Single Image Using Implicit Neural Representations
Keyu Wu, Yifan Ye, Lingchen Yang +3
Undoubtedly, high-fidelity 3D hair plays an indispensable role in digital humans. However, existing monocular hair modeling methods are either tricky to deploy in digital systems (…
AI-enabled Automatic Multimodal Fusion of Cone-Beam CT and Intraoral Scans for Intelligent 3D Tooth-Bone Reconstruction and Clinical Applications
Jin Hao, Jiaxiang Liu, Jin Li +17
A critical step in virtual dental treatment planning is to accurately delineate all tooth-bone structures from CBCT with high fidelity and accurate anatomical information. Previous…
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…
Can We Use Neural Regularization to Solve Depth Super-Resolution?
Milena Gazdieva, Oleg Voynov, Alexey Artemov +3
Depth maps captured with commodity sensors often require super-resolution to be used in applications. In this work we study a super-resolution approach based on a variational probl…
Domain Adaptation on Point Clouds via Geometry-Aware Implicits
Yuefan Shen, Yanchao Yang, Mi Yan +3
As a popular geometric representation, point clouds have attracted much attention in 3D vision, leading to many applications in autonomous driving and robotics. One important yet u…
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…
GCN-Denoiser: Mesh Denoising with Graph Convolutional Networks
Yuefan Shen, Hongbo Fu, Zhongshuo Du +5
In this paper, we present GCN-Denoiser, a novel feature-preserving mesh denoising method based on graph convolutional networks (GCNs). Unlike previous learning-based mesh denoising…
ADeLA: Automatic Dense Labeling with Attention for Viewpoint Adaptation in Semantic Segmentation
Yanchao Yang, Hanxiang Ren, He Wang +5
We describe an unsupervised domain adaptation method for image content shift caused by viewpoint changes for a semantic segmentation task. Most existing methods perform domain alig…
DCL: Differential Contrastive Learning for Geometry-Aware Depth Synthesis
Yuefan Shen, Yanchao Yang, Youyi Zheng +2
We describe a method for unpaired realistic depth synthesis that learns diverse variations from the real-world depth scans and ensures geometric consistency between the synthetic a…
AutoSweep: Recovering 3D Editable Objectsfrom a Single Photograph
Xin Chen, Yuwei Li, Xi Luo +4
This paper presents a fully automatic framework for extracting editable 3D objects directly from a single photograph. Unlike previous methods which recover either depth maps, point…
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…
Latent-Space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds
Vage Egiazarian, Savva Ignatyev, Alexey Artemov +5
Constructing high-quality generative models for 3D shapes is a fundamental task in computer vision with diverse applications in geometry processing, engineering, and design. Despit…
DeepSketchHair: Deep Sketch-based 3D Hair Modeling
Yuefan Shen, Changgeng Zhang, Hongbo Fu +2
We present sketchhair, a deep learning based tool for interactive modeling of 3D hair from 2D sketches. Given a 3D bust model as reference, our sketching system takes as input a us…
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…
Hair-GANs: Recovering 3D Hair Structure from a Single Image
Meng Zhang, Youyi Zheng
We introduce Hair-GANs, an architecture of generative adversarial networks, to recover the 3D hair structure from a single image. The goal of our networks is to build a parametric…
Recovering 3D Planar Arrangements from Videos
Shuai Du, Youyi Zheng
Acquiring 3D geometry of real world objects has various applications in 3D digitization, such as navigation and content generation in virtual environments. Image remains one of the…
Ergonomic-driven Geometric Exploration and Reshaping
Youyi Zheng, Julie Dorsey, Niloy Mitra
The paper addresses the following problem: given a set of man-made shapes, e.g., chairs, can we quickly rank and explore the set of shapes with respect to a given avatar pose? Answ…