1 code implementation • 23 Aug 2023 • Yufeng Yin, Di Chang, Guoxian Song, Shen Sang, Tiancheng Zhi, Jing Liu, Linjie Luo, Mohammad Soleymani
The proposed FG-Net achieves a strong generalization ability for heatmap-based AU detection thanks to the generalizable and semantic-rich features extracted from the pre-trained generative model.
no code implementations • CVPR 2023 • Hongyi Xu, Guoxian Song, Zihang Jiang, Jianfeng Zhang, Yichun Shi, Jing Liu, WanChun Ma, Jiashi Feng, Linjie Luo
We present OmniAvatar, a novel geometry-guided 3D head synthesis model trained from in-the-wild unstructured images that is capable of synthesizing diverse identity-preserved 3D heads with compelling dynamic details under full disentangled control over camera poses, facial expressions, head shapes, articulated neck and jaw poses.
no code implementations • 24 Mar 2023 • Guoxian Song, Hongyi Xu, Jing Liu, Tiancheng Zhi, Yichun Shi, Jianfeng Zhang, Zihang Jiang, Jiashi Feng, Shen Sang, Linjie Luo
Capitalizing on the recent advancement of 3D-aware GAN models, we perform \emph{guided transfer learning} on a pretrained 3D GAN generator to produce multi-view-consistent stylized renderings.
1 code implementation • 23 Mar 2023 • Sizhe An, Hongyi Xu, Yichun Shi, Guoxian Song, Umit Ogras, Linjie Luo
We propose PanoHead, the first 3D-aware generative model that enables high-quality view-consistent image synthesis of full heads in $360^\circ$ with diverse appearance and detailed geometry using only in-the-wild unstructured images for training.
1 code implementation • CVPR 2023 • Sizhe An, Hongyi Xu, Yichun Shi, Guoxian Song, Umit Y. Ogras, Linjie Luo
We propose PanoHead, the first 3D-aware generative model that enables high-quality view-consistent image synthesis of full heads in 360deg with diverse appearance and detailed geometry using only in-the-wild unstructured images for training.
no code implementations • 15 Nov 2022 • Shen Sang, Tiancheng Zhi, Guoxian Song, Minghao Liu, Chunpong Lai, Jing Liu, Xiang Wen, James Davis, Linjie Luo
We propose a novel self-supervised learning framework to create high-quality stylized 3D avatars with a mix of continuous and discrete parameters.
no code implementations • 13 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.
no code implementations • 5 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.
1 code implementation • ACM Transactions on Graphics 2021 • Guoxian Song, Linjie Luo, Jing Liu, Wan-Chun Ma, Chun-Pong Lai, Chuanxia Zheng, Tat-Jen Cham
While substantial progress has been made in automated stylization, generating high quality stylistic portraits is still a challenge, and even the recent popular Toonify suffers from several artifacts when used on real input images.
no code implementations • 30 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.
1 code implementation • 7 Aug 2020 • Yichao Zhou, Jingwei Huang, Xili Dai, Shichen Liu, Linjie Luo, Zhili Chen, Yi Ma
We present HoliCity, a city-scale 3D dataset with rich structural information.
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.
Ranked #3 on
2D Human Pose Estimation
on JHMDB (2D poses only)
no code implementations • 16 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.
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).
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.
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.
no code implementations • 6 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.
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.
Ranked #2 on
Keypoint Detection
on Pascal3D+
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.
no code implementations • 17 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.
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.