no code implementations • 9 May 2022 • Zizheng Yan, Yushuang Wu, Guanbin Li, Yipeng Qin, Xiaoguang Han, Shuguang Cui
Semi-supervised domain adaptation (SSDA) aims to apply knowledge learned from a fully labeled source domain to a scarcely labeled target domain.
no code implementations • 25 Apr 2022 • Liangdong Qiu, Chongjie Ye, Pei Chen, Yunbi Liu, Xiaoguang Han, Shuguang Cui
Experimental results on $4, 773$ dental models have shown our DArch can accurately segment each tooth of a dental model, and its performance is superior to the state-of-the-art methods.
no code implementations • 28 Mar 2022 • Heming Zhu, Lingteng Qiu, Yuda Qiu, Xiaoguang Han
Fueled by the power of deep learning techniques and implicit shape learning, recent advances in single-image human digitalization have reached unprecedented accuracy and could recover fine-grained surface details such as garment wrinkles.
no code implementations • 24 Mar 2022 • Chenming Zhu, Xuanye Zhang, Yanran Li, Liangdong Qiu, Kai Han, Xiaoguang Han
Contour-based models are efficient and generic to be incorporated with any existing segmentation methods, but they often generate over-smoothed contour and tend to fail on corner areas.
no code implementations • 24 Mar 2022 • Chaoqi Chen, Jiongcheng Li, Xiaoguang Han, Xiaoqing Liu, Yizhou Yu
Such holistic semantic structure, referred to as meta-knowledge here, is crucial for learning generalizable representations.
no code implementations • 19 Mar 2022 • Kun Zhou, Wenbo Li, Xiaoguang Han, Jiangbo Lu
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 Vimeo90K
no code implementations • 17 Mar 2022 • Mutian Xu, Pei Chen, Haolin Liu, Xiaoguang Han
Many basic indoor activities such as eating or writing are always conducted upon different tabletops (e. g., coffee tables, writing desks).
1 code implementation • 26 Feb 2022 • Chaofeng Chen, Xinyu Shi, Yipeng Qin, Xiaoming Li, Xiaoguang Han, Tao Yang, Shihui Guo
Since features in the codebook have shown the ability to generate natural textures in the pretrain stage, QuanTexSR can generate rich and realistic textures with the pretrained codebook as texture priors.
no code implementations • 22 Feb 2022 • Yushuang Wu, Zizheng Yan, Shengcai Cai, Guanbin Li, Yizhou Yu, Xiaoguang Han, Shuguang Cui
Semantic segmentation of point cloud usually relies on dense annotation that is exhausting and costly, so it attracts wide attention to investigate solutions for the weakly supervised scheme with only sparse points annotated.
no code implementations • 10 Feb 2022 • Xianggang Yu, Jiapeng Tang, Yipeng Qin, Chenghong Li, Linchao Bao, Xiaoguang Han, Shuguang Cui
We present PVSeRF, a learning framework that reconstructs neural radiance fields from single-view RGB images, for novel view synthesis.
no code implementations • 1 Dec 2021 • Yinyu Nie, Angela Dai, Xiaoguang Han, Matthias Nießner
To this end, we propose P2R-Net to learn a probabilistic 3D model of the objects in a scene characterized by their class categories and oriented 3D bounding boxes, based on an input observed human trajectory in the environment.
1 code implementation • 30 Nov 2021 • Kun Zhou, Wenbo Li, Liying Lu, Xiaoguang Han, Jiangbo Lu
Long-range temporal alignment is critical yet challenging for video restoration tasks.
Ranked #1 on
Video Super-Resolution
on Vimeo-90K
no code implementations • ACM International Conference on Multimedia 2021 • Zizheng Yan, Xianggang Yu, Yipeng Qin, Yushuang Wu, Xiaoguang Han, Shuguang Cui
Recent advances in unsupervised domain adaptation have achieved remarkable performance on semantic segmentation tasks.
no code implementations • 16 Sep 2021 • Chufeng Xiao, Deng Yu, Xiaoguang Han, Youyi Zheng, Hongbo Fu
At the second stage, another network is trained to synthesize the structure and appearance of hair images from the input sketch and the generated matte.
1 code implementation • ICCV 2021 • Hong-Yu Zhou, Chixiang Lu, Sibei Yang, Xiaoguang Han, Yizhou Yu
From this perspective, we introduce Preservational Learning to reconstruct diverse image contexts in order to preserve more information in learned representations.
1 code implementation • ICCV 2021 • Bingchen Gong, Yinyu Nie, Yiqun Lin, Xiaoguang Han, Yizhou Yu
Main-stream methods predict the missing shapes by decoding a global feature learned from the input point cloud, which often leads to deficient results in preserving topology consistency and surface details.
1 code implementation • 5 Aug 2021 • Zhongjin Luo, Jie zhou, Heming Zhu, Dong Du, Xiaoguang Han, Hongbo Fu
In this work, we propose SimpModeling, a novel sketch-based system for helping users, especially amateur users, easily model 3D animalmorphic heads - a prevalent kind of heads in character design.
no code implementations • 21 Jul 2021 • Mengcheng Lan, Shuliang Ning, Yanran Li, Qian Chen, Xunlai Chen, Xiaoguang Han, Shuguang Cui
Despite video forecasting has been a widely explored topic in recent years, the mainstream of the existing work still limits their models with a single prediction space but completely neglects the way to leverage their model with multi-prediction spaces.
no code implementations • 14 Jul 2021 • Xinda Liu, Lili Wang, Xiaoguang Han
In this paper, we analyze the difficulties of fine-grained image recognition from a new perspective and propose a transformer architecture with the peak suppression module and knowledge guidance module, which respects the diversification of discriminative features in a single image and the aggregation of discriminative clues among multiple images.
Ranked #6 on
Fine-Grained Image Classification
on CUB-200-2011
Fine-Grained Image Classification
Fine-Grained Image Recognition
no code implementations • 9 Jul 2021 • Yiqun Lin, Lichang Chen, Haibin Huang, Chongyang Ma, Xiaoguang Han, Shuguang Cui
Sampling, grouping, and aggregation are three important components in the multi-scale analysis of point clouds.
no code implementations • 9 Jul 2021 • Wenting Jiang, Yicheng Jiang, Lu Zhang, Changmiao Wang, Xiaoguang Han, Shuixing Zhang, Xiang Wan, Shuguang Cui
In this paper, we raise the problem of HCC segmentation in DSA videos, and build our own DSA dataset.
no code implementations • CVPR 2021 • Yuda Qiu, Xiaojie Xu, Lingteng Qiu, Yan Pan, Yushuang Wu, Weikai Chen, Xiaoguang Han
Caricature is an artistic representation that deliberately exaggerates the distinctive features of a human face to convey humor or sarcasm.
1 code implementation • CVPR 2021 • Haolin Liu, Anran Lin, Xiaoguang Han, Lei Yang, Yizhou Yu, Shuguang Cui
Grounding referring expressions in RGBD image has been an emerging field.
no code implementations • ICCV 2021 • Yushuang Wu, Zizheng Yan, Xiaoguang Han, Guanbin Li, Changqing Zou, Shuguang Cui
The key point of language-guided person search is to construct the cross-modal association between visual and textual input.
1 code implementation • CVPR 2021 • Yinyu Nie, Ji Hou, Xiaoguang Han, Matthias Nießner
In this work, we introduce RfD-Net that jointly detects and reconstructs dense object surfaces directly from raw point clouds.
no code implementations • 16 Nov 2020 • Jinmiao Cai, Nianjuan Jiang, Xiaoguang Han, Kui Jia, Jiangbo Lu
Skeleton-based action recognition has attracted research attentions in recent years.
no code implementations • NeurIPS 2020 • Yinyu Nie, Yiqun Lin, Xiaoguang Han, Shihui Guo, Jian Chang, Shuguang Cui, Jian Jun Zhang
Existing works usually estimate the missing shape by decoding a latent feature encoded from the input points.
no code implementations • 9 Oct 2020 • Yao Li, Xianggang Yu, Xiaoguang Han, Nianjuan Jiang, Kui Jia, Jiangbo Lu
In this work, we propose an interactive system to design diverse high-quality garment images from fashion sketches and the texture information.
no code implementations • 18 Sep 2020 • Jie Wu, Guanbin Li, Xiaoguang Han, Liang Lin
Temporal grounding of natural language in untrimmed videos is a fundamental yet challenging multimedia task facilitating cross-media visual content retrieval.
no code implementations • 10 Sep 2020 • Jiali Liu, Wenxuan Wang, Tianyao Guan, Ningbo Zhao, Xiaoguang Han, Zhen Li
An indicator-guided learning mechanism is further proposed to ease the training of the proposed model.
1 code implementation • 13 Aug 2020 • Jiapeng Tang, Xiaoguang Han, Mingkui Tan, Xin Tong, Kui Jia
However, they all have their own drawbacks, and cannot properly reconstruct the surface shapes of complex topologies, arguably due to a lack of constraints on the topologicalstructures in their learning frameworks.
2 code implementations • ECCV 2020 • Heming Zhu, Yu Cao, Hang Jin, Weikai Chen, Dong Du, Zhangye Wang, Shuguang Cui, Xiaoguang Han
High-fidelity clothing reconstruction is the key to achieving photorealism in a wide range of applications including human digitization, virtual try-on, etc.
1 code implementation • 26 Mar 2020 • Yuda Qiu, Zhangyang Xiong, Kai Han, Zhongyuan Wang, Zixiang Xiong, Xiaoguang Han
To alleviate this problem, we propose a weakly supervised training approach to train our model on real face videos, based on the assumption of consistency of albedo and normal across different frames, thus bridging the gap between real and synthetic face images.
1 code implementation • ECCV 2020 • Lingteng Qiu, Xuanye Zhang, Yan-ran Li, Guanbin Li, Xiao-Jun Wu, Zixiang Xiong, Xiaoguang Han, Shuguang Cui
Although occlusion widely exists in nature and remains a fundamental challenge for pose estimation, existing heatmap-based approaches suffer serious degradation on occlusions.
1 code implementation • 10 Mar 2020 • Kun Zhou, Xiaoguang Han, Nianjuan Jiang, Kui Jia, Jiangbo Lu
Estimating 3D human pose from a single image is a challenging task.
1 code implementation • CVPR 2020 • Yinyu Nie, Xiaoguang Han, Shihui Guo, Yujian Zheng, Jian Chang, Jian Jun Zhang
Semantic reconstruction of indoor scenes refers to both scene understanding and object reconstruction.
Ranked #2 on
Room Layout Estimation
on SUN RGB-D
(using extra training data)
1 code implementation • CVPR 2020 • Yiqun Lin, Zizheng Yan, Haibin Huang, Dong Du, Ligang Liu, Shuguang Cui, Xiaoguang Han
We introduce FPConv, a novel surface-style convolution operator designed for 3D point cloud analysis.
no code implementations • 22 Feb 2020 • Yinyu Nie, Shihui Guo, Jian Chang, Xiaoguang Han, Jiahui Huang, Shi-Min Hu, Jian Jun Zhang
Particularly, we design a shallow-to-deep architecture on the basis of convolutional networks for semantic scene understanding and modeling.
no code implementations • 23 Nov 2019 • Chaowei Fang, Guanbin Li, Xiaoguang Han, Yizhou Yu
It further recurrently exploits the reconstructed results and intermediate features of a sequence of preceding frames to improve the initial super-resolution of the current frame by modelling the coherence of structural facial features across frames.
no code implementations • ICCV 2019 • Kun Zhou, Xiaoguang Han, Nianjuan Jiang, Kui Jia, Jiangbo Lu
Estimating 3D human pose from a single image is a challenging task.
no code implementations • ICCV 2019 • Junyi Pan, Xiaoguang Han, Weikai Chen, Jiapeng Tang, Kui Jia
The key to our approach is a novel progressive shaping framework that alternates between mesh deformation and topology modification.
1 code implementation • CVPR 2019 2019 • Jiapeng Tang, Xiaoguang Han, Junyi Pan, Kui Jia, Xin Tong
To this end, we propose in this paper a skeleton-bridged, stage-wise learning approach to address the challenge.
1 code implementation • CVPR 2019 • Jiapeng Tang, Xiaoguang Han, Junyi Pan, Kui Jia, Xin Tong
To this end, we propose in this paper a skeleton-bridged, stage-wise learning approach to address the challenge.
no code implementations • CVPR 2019 • Xiaoguang Han, Zhaoxuan Zhang, Dong Du, Mingdai Yang, Jingming Yu, Pan Pan, Xin Yang, Ligang Liu, Zixiang Xiong, Shuguang Cui
Given a single depth image, our method first goes through the 3D volume branch to obtain a volumetric scene reconstruction as a guide to the next view inpainting step, which attempts to make up the missing information; the third step involves projecting the volume under the same view of the input, concatenating them to complete the current view depth, and integrating all depth into the point cloud.
no code implementations • 28 Feb 2019 • Haonan Qiu, Chuan Wang, Hang Zhu, Xiangyu Zhu, Jinjin Gu, Xiaoguang Han
Generating plausible hair image given limited guidance, such as sparse sketches or low-resolution image, has been made possible with the rise of Generative Adversarial Networks (GANs).
no code implementations • 18 Jan 2019 • Zizheng Yan, Xiaoguang Han, Changmiao Wang, Yuda Qiu, Zixiang Xiong, Shuguang Cui
Due to high-resolution and small-size lesion regions, applying existing methods, such as U-Nets, to perform segmentation on fundus photography is very challenging.
no code implementations • 11 Dec 2018 • Weikai Chen, Xiaoguang Han, Guanbin Li, Chao Chen, Jun Xing, Yajie Zhao, Hao Li
Three-dimensional object recognition has recently achieved great progress thanks to the development of effective point cloud-based learning frameworks, such as PointNet and its extensions.
no code implementations • 21 Sep 2018 • Kun Zhou, Jinmiao Cai, Yao Li, Yulong Shi, Xiaoguang Han, Nianjuan Jiang, Kui Jia, Jiangbo Lu
In this paper, a novel deep-learning based framework is proposed to infer 3D human poses from a single image.
no code implementations • 24 Jul 2018 • Xiaoguang Han, Kangcheng Hou, Dong Du, Yuda Qiu, Yizhou Yu, Kun Zhou, Shuguang Cui
To construct the mapping between 2D sketches and a vertex-wise scaling field, a novel deep learning architecture is developed.
no code implementations • 25 Jun 2018 • Yulong Shi, Xiaoguang Han, Nianjuan Jiang, Kun Zhou, Kui Jia, Jiangbo Lu
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.
no code implementations • 22 Jun 2018 • Chuan Wang, Haibin Huang, Xiaoguang Han, Jue Wang
We present a new data-driven video inpainting method for recovering missing regions of video frames.
no code implementations • ICCV 2017 • Xiaoguang Han, Zhen Li, Haibin Huang, Evangelos Kalogerakis, Yizhou Yu
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.
no code implementations • 7 Jun 2017 • Xiaoguang Han, Chang Gao, Yizhou Yu
This system has a labor-efficient sketching interface, that allows the user to draw freehand imprecise yet expressive 2D lines representing the contours of facial features.