no code implementations • 9 Mar 2023 • Yuxin Zhang, Fan Tang, WeiMing Dong, Haibin Huang, Chongyang Ma, Tong-Yee Lee, Changsheng Xu
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.
no code implementations • 5 Mar 2023 • Yujian Zheng, Zirong Jin, Moran Li, Haibin Huang, Chongyang Ma, Shuguang Cui, Xiaoguang Han
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.
1 code implementation • 28 Feb 2023 • Xueyi Liu, Ji Zhang, Ruizhen Hu, Haibin Huang, He Wang, Li Yi
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.
1 code implementation • 15 Jan 2023 • Yiqin Zhao, Chongyang Ma, Haibin Huang, Tian Guo
In this work, we present the design and implementation of a lighting reconstruction framework called LitAR that enables realistic and visually-coherent rendering.
1 code implementation • 23 Nov 2022 • Yuxin Zhang, Nisha Huang, Fan Tang, Haibin Huang, Chongyang Ma, WeiMing Dong, Changsheng Xu
Our key idea is to learn artistic style directly from a single painting and then guide the synthesis without providing complex textual descriptions.
1 code implementation • 19 Nov 2022 • Nisha Huang, Yuxin Zhang, Fan Tang, Chongyang Ma, Haibin Huang, Yong Zhang, WeiMing Dong, Changsheng Xu
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.
1 code implementation • 19 May 2022 • Yuxin Zhang, Fan Tang, WeiMing Dong, Haibin Huang, Chongyang Ma, Tong-Yee Lee, Changsheng Xu
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.
no code implementations • 6 Mar 2022 • Yisheng He, Haoqiang Fan, Haibin Huang, Qifeng Chen, Jian Sun
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.
no code implementations • 17 Feb 2022 • Zejia Su, Haibin Huang, Chongyang Ma, Hui Huang, Ruizhen Hu
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.
2 code implementations • CVPR 2022 • Ziwei Luo, Haibin Huang, Lei Yu, Youwei Li, Haoqiang Fan, Shuaicheng Liu
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
no code implementations • 5 Dec 2021 • Moran Li, Haibin Huang, Yi Zheng, Mengtian Li, Nong Sang, Chongyang Ma
In this work, we present a new method for 3D face reconstruction from sparse-view RGB images.
no code implementations • 30 Sep 2021 • Lei Yang, Yan Zi Wei, Yisheng He, Wei Sun, Zhenhang Huang, Haibin Huang, Haoqiang Fan
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
1 code implementation • ICCV 2021 • Haitao Yang, Zaiwei Zhang, Siming Yan, Haibin Huang, Chongyang Ma, Yi Zheng, Chandrajit Bajaj, QiXing Huang
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.
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.
1 code implementation • ICCV 2021 • Siming Yan, Zhenpei Yang, Chongyang Ma, Haibin Huang, Etienne Vouga, QiXing Huang
This paper introduces HPNet, a novel deep-learning approach for segmenting a 3D shape represented as a point cloud into primitive patches.
1 code implementation • 26 Mar 2021 • Youwei Li, Haibin Huang, Lanpeng Jia, Haoqiang Fan, Shuaicheng Liu
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.
3 code implementations • CVPR 2021 • Yisheng He, Haibin Huang, Haoqiang Fan, Qifeng Chen, Jian Sun
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
3 code implementations • CVPR 2021 • Shen Cheng, Yuzhi Wang, Haibin Huang, Donghao Liu, Haoqiang Fan, Shuaicheng Liu
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)
1 code implementation • ECCV 2020 • Yuzhi Wang, Haibin Huang, Qin Xu, Jiaming Liu, Yiqun Liu, Jue Wang
Deep learning-based image denoising approaches have been extensively studied in recent years, prevailing in many public benchmark datasets.
no code implementations • 17 Sep 2020 • Yingying Deng, Fan Tang, Wei-Ming Dong, Haibin Huang, Chongyang Ma, Changsheng Xu
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.
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.
1 code implementation • 6 Dec 2019 • Albert Zhao, Tong He, Yitao Liang, Haibin Huang, Guy Van Den Broeck, Stefano Soatto
To learn this representation, we train a squeeze network to drive using annotations for the side task as input.
3 code implementations • CVPR 2020 • Yisheng He, Wei Sun, Haibin Huang, Jianran Liu, Haoqiang Fan, Jian Sun
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)
no code implementations • ICCV 2019 • Shaofan Cai, Xiaoshuai Zhang, Haoqiang Fan, Haibin Huang, Jiangyu Liu, Jiaming Liu, Jiaying Liu, Jue Wang, Jian Sun
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.
no code implementations • ICCV 2019 • Yi He, Jiayuan Shi, Chuan Wang, Haibin Huang, Jiaming Liu, Guanbin Li, Risheng Liu, Jue Wang
In this paper we present a new data-driven method for robust skin detection from a single human portrait image.
no code implementations • 8 May 2019 • Yifan Ding, Chuan Wang, Haibin Huang, Jiaming Liu, Jue Wang, Liqiang Wang
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.
1 code implementation • 29 Apr 2019 • Jiaming Liu, Chi-Hao Wu, Yuzhi Wang, Qin Xu, Yuqian Zhou, Haibin Huang, Chuan Wang, Shaofan Cai, Yifan Ding, Haoqiang Fan, Jue Wang
In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising.
2 code implementations • 6 Apr 2019 • Yuqian Zhou, Jianbo Jiao, Haibin Huang, Yang Wang, Jue Wang, Honghui Shi, Thomas Huang
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
1 code implementation • ACM Transactions on Graphics (Proc. SIGGRAPH ASIA) 2019 • Hao Wang, Nadav Schor, Ruizhen Hu, Haibin Huang, Daniel Cohen-Or, Hui Huang
We also introduce new means to measure and evaluate the performance of an adversarial generative model.
no code implementations • CVPR 2019 • Yang Wang, Haibin Huang, Chuan Wang, Tong He, Jue Wang, Minh Hoai
In this paper, we propose GIF2Video, the first learning-based method for enhancing the visual quality of GIFs in the wild.
no code implementations • CVPR 2019 • Tong He, Haibin Huang, Li Yi, Yuqian Zhou, Chi-Hao Wu, Jue Wang, Stefano Soatto
Surface-based geodesic topology provides strong cues for object semantic analysis and geometric modeling.
1 code implementation • SIGGRAPH Asia 2018 2018 • Kekai Sheng, Wei-Ming Dong, Haibin Huang, Chongyang Ma, Bao-Gang Hu
In this study, we present the Gourmet Photography Dataset (GPD), which is the first large-scale dataset for aesthetic assessment of food photographs.
1 code implementation • 19 Sep 2018 • Li Yi, Haibin Huang, Difan Liu, Evangelos Kalogerakis, Hao Su, Leonidas Guibas
In this paper, we explore how the observation of different articulation states provides evidence for part structure and motion of 3D objects.
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.
1 code implementation • 17 Oct 2017 • Li Yi, Lin Shao, Manolis Savva, Haibin Huang, Yang Zhou, Qirui Wang, Benjamin Graham, Martin Engelcke, Roman Klokov, Victor Lempitsky, Yuan Gan, Pengyu Wang, Kun Liu, Fenggen Yu, Panpan Shui, Bingyang Hu, Yan Zhang, Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Minki Jeong, Jaehoon Choi, Changick Kim, Angom Geetchandra, Narasimha Murthy, Bhargava Ramu, Bharadwaj Manda, M. Ramanathan, Gautam Kumar, P Preetham, Siddharth Srivastava, Swati Bhugra, Brejesh lall, Christian Haene, Shubham Tulsiani, Jitendra Malik, Jared Lafer, Ramsey Jones, Siyuan Li, Jie Lu, Shi Jin, Jingyi Yu, Qi-Xing Huang, Evangelos Kalogerakis, Silvio Savarese, Pat Hanrahan, Thomas Funkhouser, Hao Su, Leonidas Guibas
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.
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 • CVPR 2017 • Amir Arsalan Soltani, Haibin Huang, Jiajun Wu, Tejas D. Kulkarni, Joshua B. Tenenbaum
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.
no code implementations • 14 Jun 2017 • Haibin Huang, Evangelos Kalogerakis, Siddhartha Chaudhuri, Duygu Ceylan, Vladimir G. Kim, Ersin Yumer
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.