Search Results for author: Linjie Luo

Found 14 papers, 5 papers with code

Using Augmented Face Images to Improve Facial Recognition Tasks

no code implementations13 May 2022 Shuo Cheng, Guoxian Song, Wan-Chun Ma, Chao Wang, Linjie Luo

We present a framework that uses GAN-augmented images to complement certain specific attributes, usually underrepresented, for machine learning model training.

High-Quality Pluralistic Image Completion via Code Shared VQGAN

no code implementations5 Apr 2022 Chuanxia Zheng, Guoxian Song, Tat-Jen Cham, Jianfei Cai, Dinh Phung, Linjie Luo

In this work, we present a novel framework for pluralistic image completion that can achieve both high quality and diversity at much faster inference speed.

Image Reconstruction

DynOcc: Learning Single-View Depth from Dynamic Occlusion Cues

no code implementations30 Mar 2021 Yifan Wang, Linjie Luo, Xiaohui Shen, Xing Mei

Recently, significant progress has been made in single-view depth estimation thanks to increasingly large and diverse depth datasets.

3D Reconstruction Autonomous Driving +2

Dynamic Kernel Distillation for Efficient Pose Estimation in Videos

no code implementations ICCV 2019 Xuecheng Nie, Yuncheng Li, Linjie Luo, Ning Zhang, Jiashi Feng

Existing video-based human pose estimation methods extensively apply large networks onto every frame in the video to localize body joints, which suffer high computational cost and hardly meet the low-latency requirement in realistic applications.

Frame Pose Estimation

Task-Assisted Domain Adaptation with Anchor Tasks

no code implementations16 Aug 2019 Zhizhong Li, Linjie Luo, Sergey Tulyakov, Qieyun Dai, Derek Hoiem

Our key idea to improve domain adaptation is to introduce a separate anchor task (such as facial landmarks) whose annotations can be obtained at no cost or are already available on both synthetic and real datasets.

Depth Estimation Domain Adaptation +1

Transformable Bottleneck Networks

1 code implementation ICCV 2019 Kyle Olszewski, Sergey Tulyakov, Oliver Woodford, Hao Li, Linjie Luo

We propose a novel approach to performing fine-grained 3D manipulation of image content via a convolutional neural network, which we call the Transformable Bottleneck Network (TBN).

3D Reconstruction Novel View Synthesis

Extreme Relative Pose Estimation for RGB-D Scans via Scene Completion

1 code implementation CVPR 2019 Zhenpei Yang, Jeffrey Z. Pan, Linjie Luo, Xiaowei Zhou, Kristen Grauman, Qi-Xing Huang

In particular, instead of only performing scene completion from each individual scan, our approach alternates between relative pose estimation and scene completion.

Pose Estimation

Deep Volumetric Video From Very Sparse Multi-View Performance Capture

no code implementations ECCV 2018 Zeng Huang, Tianye Li, Weikai Chen, Yajie Zhao, Jun Xing, Chloe LeGendre, Linjie Luo, Chongyang Ma, Hao Li

We present a deep learning-based volumetric capture approach for performance capture using a passive and highly sparse multi-view capture system.

Frame Surface Reconstruction

Deep Generative Modeling for Scene Synthesis via Hybrid Representations

no code implementations6 Aug 2018 Zaiwei Zhang, Zhenpei Yang, Chongyang Ma, Linjie Luo, Alexander Huth, Etienne Vouga, Qi-Xing Huang

We show a principled way to train this model by combining discriminator losses for both a 3D object arrangement representation and a 2D image-based representation.

StarMap for Category-Agnostic Keypoint and Viewpoint Estimation

1 code implementation ECCV 2018 Xingyi Zhou, Arjun Karpur, Linjie Luo, Qi-Xing Huang

Existing methods define semantic keypoints separately for each category with a fixed number of semantic labels in fixed indices.

Keypoint Detection Viewpoint Estimation

Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency

1 code implementation ECCV 2018 Xingyi Zhou, Arjun Karpur, Chuang Gan, Linjie Luo, Qi-Xing Huang

In this paper, we introduce a novel unsupervised domain adaptation technique for the task of 3D keypoint prediction from a single depth scan or image.

Unsupervised Domain Adaptation

AutoScaler: Scale-Attention Networks for Visual Correspondence

no code implementations17 Nov 2016 Shenlong Wang, Linjie Luo, Ning Zhang, Jia Li

We propose AutoScaler, a scale-attention network to explicitly optimize this trade-off in visual correspondence tasks.

Optical Flow Estimation

Wide-Baseline Hair Capture Using Strand-Based Refinement

no code implementations CVPR 2013 Linjie Luo, Cha Zhang, Zhengyou Zhang, Szymon Rusinkiewicz

We propose a novel algorithm to reconstruct the 3D geometry of human hairs in wide-baseline setups using strand-based refinement.

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