Search Results for author: Haibin Huang

Found 28 papers, 14 papers with code

Implicit Neural Deformation for Multi-View Face Reconstruction

no code implementations5 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 multi-view RGB images.

3D Face Reconstruction

iShape: A First Step Towards Irregular Shape Instance Segmentation

no code implementations30 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.

Instance Segmentation Semantic Segmentation

Scene Synthesis via Uncertainty-Driven Attribute Synchronization

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.

Beyond Farthest Point Sampling in Point-Wise Analysis

no code implementations9 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.

Keypoint Detection Point Cloud Completion

HPNet: Deep Primitive Segmentation Using Hybrid Representations

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.

D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution

no code implementations26 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.

Image Super-Resolution SSIM

FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation

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.

6D Pose Estimation Representation Learning

NBNet: Noise Basis Learning for Image Denoising with Subspace Projection

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.

Image Denoising SSIM

Practical Deep Raw Image Denoising on Mobile Devices

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.

Image Denoising

Arbitrary Video Style Transfer via Multi-Channel Correlation

no code implementations17 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.

Style Transfer Video Style Transfer

SAM: Squeeze-and-Mimic Networks for Conditional Visual Driving Policy Learning

1 code implementation6 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.

Semantic Segmentation

Disentangled Image Matting

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.

Image Matting

Semi-supervised Skin Detection by Network with Mutual Guidance

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.

Frame-Recurrent Video Inpainting by Robust Optical Flow Inference

no code implementations8 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.

Image Inpainting Optical Flow Estimation +1

When AWGN-based Denoiser Meets Real Noises

2 code implementations6 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.

Denoising

GIF2Video: Color Dequantization and Temporal Interpolation of GIF images

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.

Quantization

Gourmet Photography Dataset for Aesthetic Assessment of Food Images

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.

Deep Part Induction from Articulated Object Pairs

1 code implementation19 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.

Video Inpainting by Jointly Learning Temporal Structure and Spatial Details

no code implementations22 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.

Video Inpainting

High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference

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.

Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes With Deep Generative Networks

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.

Learning Local Shape Descriptors from Part Correspondences With Multi-view Convolutional Networks

no code implementations14 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.

Semantic Segmentation

Cannot find the paper you are looking for? You can Submit a new open access paper.