We propose to learn a fully-convolutional network model that consists of a Chain of Identity Mapping Modules (CIMM) for image denoising. The CIMM structure possesses two distinctive features that are important for the noise removal task. Firstly, each residual unit employs identity mappings as the skip connections and receives pre-activated input in order to preserve the gradient magnitude propagated in both the forward and backward directions. Secondly, by utilizing dilated kernels for the convolution layers in the residual branch, in other words within an identity mapping module, each neuron in the last convolution layer can observe the full receptive field of the first layer. After being trained on the BSD400 dataset, the proposed network produces remarkably higher numerical accuracy and better visual image quality than the state-of-the-art when being evaluated on conventional benchmark images and the BSD68 dataset.
We propose to learn a fully-convolutional network model that consists of a Chain of Identity Mapping Modules and residual on the residual architecture for image denoising. Our network structure possesses three distinctive features that are important for the noise removal task. Firstly, each unit employs identity mappings as the skip connections and receives pre-activated input to preserve the gradient magnitude propagated in both the forward and backward directions. Secondly, by utilizing dilated kernels for the convolution layers in the residual branch, each neuron in the last convolution lay…
MFR-Net: Multi-faceted Responsive Listening Head Generation via Denoising Diffusion Model
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
Cycle-Constrained Adversarial Denoising Convolutional Network for PET Image Denoising: Multi-Dimensional Validation on Large Datasets with Reader Study and Real Low-Dose Data
Nanjing University of Posts and Telecommunications（南京邮电大学）；Soochow University（苏州大学）；Kyushu Institute of Technology（九州工业大学）；Nanjing University of Science and Technology（南京理工大学）
With the growing importance of preventing the COVID-19 virus, face images obtained in most video surveillance scenarios are low resolution with mask simultaneously. However, most of the previous face super-resolution solutions can not handle both tasks in one model. In this work, we treat the mask occlusion as image noise and construct a joint and collaborative learning network, called JDSR-GAN, for the masked face super-resolution task. Given a low-quality face image with the mask as input, the role of the generator composed of a denoising module and super-resolution module is to acquire a hi…
Towards Consistent and Controllable Image Synthesis for Face Editing
Center for Machine Vision and Signal Analysis, Faculty of Information Technology and Electrical Engineering, University of Oulu（机器视觉与信号分析中心，信息科技与电气工程学院，奥卢大学）；Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Sciences and Medical Engineering, Southeast University（教育部儿童发展与学习科学重点实验室，生物科学与医学工程学院，东南大学）
StableAnimator: High-Quality Identity-Preserving Human Image Animation
Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University（复旦大学计算机学院上海市智能信息处理重点实验室）；Shanghai Collaborative Innovation Center of Intelligent Visual Computing（上海智能视觉计算协同创新中心）；Microsoft Research Asia（微软亚洲研究院）；Huya Inc.（虎牙公司）；Carnegie Mellon University（卡内基梅隆大学）
Hong Kong Polytechnic University（香港理工大学）；Harbin Institute of Technology（哈尔滨工业大学）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））
This paper presents a Deep convolutional network model for Identity-Aware Transfer (DIAT) of facial attributes. Given the source input image and the reference attribute, DIAT aims to generate a facial image that owns the reference attribute as well as keeps the same or similar identity to the input image. In general, our model consists of a mask network and an attribute transform network which work in synergy to generate a photo-realistic facial image with the reference attribute. Considering that the reference attribute may be only related to some parts of the image, the mask network is intro…
3D Priors-Guided Diffusion for Blind Face Restoration
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；Yunnan Key Laboratory of Smart Education（云南省智慧教育重点实验室）；Tencent Youtu Lab（腾讯优图实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Tencent Cloud Architecture Platform（腾讯云架构平台）
ID-Booth: Identity-consistent Face Generation with Diffusion Models
University of Ljubljana, Faculty of Computer and Information Science（卢布尔雅纳大学计算机与信息科学学院）；Fraunhofer Institute for Computer Graphics Research IGD（弗劳恩霍夫计算机图形研究机构）；Xi’an Jiaotong University, School of Cyber Science and Engineering（西安交通大学网络科学与工程学院）；Department of Computer Science, TU Darmstadt（图腾大学计算机科学系）；University of Ljubljana, Faculty of Electrical Engineering（卢布尔雅纳大学电子工程学院）
KAIST（韩国科学技术院）；University of Ulsan College of Medicine（蔚山大学医学院）
In coronary CT angiography, a series of CT images are taken at different levels of radiation dose during the examination. Although this reduces the total radiation dose, the image quality during the low-dose phases is significantly degraded. To address this problem, here we propose a novel semi-supervised learning technique that can remove the noises of the CT images obtained in the low-dose phases by learning from the CT images in the routine dose phases. Although a supervised learning approach is not possible due to the differences in the underlying heart structure in two phases, the images …
LAFR: Efficient Diffusion-based Blind Face Restoration via Latent Codebook Alignment Adapter
School of Electronic and Computer Engineering, Peking University（电子与计算机工程学院，北京大学）；Computer Vision Lab, CAIDAS & IFI, University of Würzburg（计算机视觉实验室，乌尔姆大学）
OMG: Occlusion-friendly Personalized Multi-concept Generation in Diffusion Models
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；Tencent AI Lab（腾讯AI实验室）；International Digital Economy Academy（国际数字经济学院）；Nanjing University（南京大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Shenzhen University（深圳大学）；The Hong Kong University of Science and Technology（香港科技大学）
ShanghaiTech University（上海科技大学）；Chinese Academy of Sciences（中国科学院）；Shanghai Institute of Microsystem and Information Technology（上海微系统与信息技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
We tackle human image synthesis, including human motion imitation, appearance transfer, and novel view synthesis, within a unified framework. It means that the model, once being trained, can be used to handle all these tasks. The existing task-specific methods mainly use 2D keypoints to estimate the human body structure. However, they only express the position information with no abilities to characterize the personalized shape of the person and model the limb rotations. In this paper, we propose to use a 3D body mesh recovery module to disentangle the pose and shape. It can not only model the…
