This paper presents a novel image inpainting framework for face mask removal. Although current methods have demonstrated their impressive ability in recovering damaged face images, they suffer from two main problems: the dependence on manually labeled missing regions and the deterministic result corresponding to each input. The proposed approach tackles these problems by integrating a multi-task 3D face reconstruction module with a face inpainting module. Given a masked face image, the former predicts a 3DMM-based reconstructed face together with a binary occlusion map, providing dense geometrical and textural priors that greatly facilitate the inpainting task of the latter. By gradually controlling the 3D shape parameters, our method generates high-quality dynamic inpainting results with different expressions and mouth movements. Qualitative and quantitative experiments verify the effectiveness of the proposed method.
CLEAR: Context-Aware Learning with End-to-End Mask-Free Inference for Adaptive Video Subtitle Removal
University of Electronic Science and Technology of China（电子科技大学）；University of Chinese Academy of Sciences（中国科学院大学）；Technical University of Munich（慕尼黑技术大学）；Shanghai Jiao Tong University（上海交通大学）；National University of Singapore（新加坡国立大学）
SADER: Structure-Aware Diffusion Framework with DEterministic Resampling for Multi-Temporal Remote Sensing Cloud Removal
College of Literature, Science, and the Arts, University of Michigan（文学、科学与艺术学院，密歇根大学）；School of Computer Science and Technology, Tongji University（计算机科学与技术学院，同济大学）
Nanjing University of Science and Technology（南京理工大学）
Understanding shadows from a single image spontaneously derives into two types of task in previous studies, containing shadow detection and shadow removal. In this paper, we present a multi-task perspective, which is not embraced by any existing work, to jointly learn both detection and removal in an end-to-end fashion that aims at enjoying the mutually improved benefits from each other. Our framework is based on a novel STacked Conditional Generative Adversarial Network (ST-CGAN), which is composed of two stacked CGANs, each with a generator and a discriminator. Specifically, a shadow image i…
DocShaDiffusion: Diffusion Model in Latent Space for Document Image Shadow Removal
School of Software, Northwestern Polytechnical University（软件学院，西北工业大学）；School of Computer Science and Engineering, South China University of Technology and Pazhou Lab（计算机科学与工程学院，华南理工大学及琶洲实验室）
Video object removal is a challenging task in video processing that often requires massive human efforts. Given the mask of the foreground object in each frame, the goal is to complete (inpaint) the object region and generate a video without the target object. While recently deep learning based methods have achieved great success on the image inpainting task, they often lead to inconsistent results between frames when applied to videos. In this work, we propose a novel learning-based Video Object Removal Network (VORNet) to solve the video object removal task in a spatio-temporally consistent …
RORem: Training a Robust Object Remover with Human-in-the-Loop
The Hong Kong Polytechnic University（香港理工大学）；OPPO Research Institute（OPPO研究院）；ByteDance（字节跳动）；The Hong Kong University of Science and Technology（香港科技大学）
OSOR: One-Step Diffusion Inpainting for Effect-Aware Object Removal
Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）；Xidian University（西安电子科技大学）；Tongji University（同济大学）；University of Electronic Science and Technology of China（电子科技大学）；Transsion（传音控股）
University of Macau（澳门大学）；Xi’an University of Technology（西安理工大学）
Shadow removal is an essential task for scene understanding. Many studies consider only matching the image contents, which often causes two types of ghosts: color in-consistencies in shadow regions or artifacts on shadow boundaries. In this paper, we tackle these issues in two ways. First, to carefully learn the border artifacts-free image, we propose a novel network structure named the dual hierarchically aggregation network~(DHAN). It contains a series of growth dilated convolutions as the backbone without any down-samplings, and we hierarchically aggregate multi-context features for attenti…
Bridging Knowledge Gap Between Image Inpainting and Large-Area Visible Watermark Removal
School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；Research Institute of Sun Yat-sen University in Shenzhen（中山大学深圳研究院）；Afirstsoft
Motion Artifact Removal in Pixel-Frequency Domain via Alternate Masks and Diffusion Model
Xidian University（西安电子科技大学）；Guangdong Provincial People’s Hospital（广东省人民医院）；Southern Medical University（南方医科大学）；Xiangyang No. 1 People’s Hospital（襄阳市第一人民医院）；Hubei University of Medicine（湖北医药学院）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Saarland Informatics Campus（萨尔兰信息学园区）
While great progress has been made recently in automatic image manipulation, it has been limited to object centric images like faces or structured scene datasets. In this work, we take a step towards general scene-level image editing by developing an automatic interaction-free object removal model. Our model learns to find and remove objects from general scene images using image-level labels and unpaired data in a generative adversarial network (GAN) framework. We achieve this with two key contributions: a two-stage editor architecture consisting of a mask generator and image in-painter that c…
Recent deep learning methods have achieved promising results in image shadow removal. However, their restored images still suffer from unsatisfactory boundary artifacts, due to the lack of degradation prior embedding and the deficiency in modeling capacity. Our work addresses these issues by proposing a unified diffusion framework that integrates both the image and degradation priors for highly effective shadow removal. In detail, we first propose a shadow degradation model, which inspires us to build a novel unrolling diffusion model, dubbed ShandowDiffusion. It remarkably improves the model'…
PANDORA: Pixel-wise Attention Dissolution and Latent Guidance for Zero-Shot Object Removal
University of Science, Ho Chi Minh City, Vietnam（越南胡志明市理科大学）；Vietnam National University, Ho Chi Minh City, Vietnam（越南胡志明市国家大学）；University of Dayton, Ohio, United States（美国俄亥俄州代顿大学）
Satellite images often contain a significant level of sensitive data compared to ground-view images. That is why satellite images are more likely to be intentionally manipulated to hide specific objects and structures. GAN-based approaches have been employed to create forged images with two major problems: (i) adding a new object to the scene to hide a specific object or region may create unrealistic merging with surrounding areas; and (ii) using masks on color feature images has proven to be unsuccessful in GAN-based object removal. In this paper, we tackle the problem of object removal in hi…
Huzhou University（湖州学院）；Huzhou Institute of Zhejiang University（浙江大学湖州研究院）；Zhejiang University（浙江大学）
As a special case of common object removal, image person removal is playing an increasingly important role in social media and criminal investigation domains. Due to the integrity of person area and the complexity of human posture, person removal has its own dilemmas. In this paper, we propose a novel idea to tackle these problems from the perspective of data synthesis. Concerning the lack of dedicated dataset for image person removal, two dataset production methods are proposed to automatically generate images, masks and ground truths respectively. Then, a learning framework similar to local …
Xidian University（西安电子科技大学）；Beijing Electronic Science and Technology Institute（北京电子科技学院）；The Chinese University of Hong Kong（香港中文大学）；Guangxi Normal University（广西师范大学）
Image restoration under severe weather is a challenging task. Most of the past works focused on removing rain and haze phenomena in images. However, snow is also an extremely common atmospheric phenomenon that will seriously affect the performance of high-level computer vision tasks, such as object detection and semantic segmentation. Recently, some methods have been proposed for snow removing, and most methods deal with snow images directly as the optimization object. However, the distribution of snow location and shape is complex. Therefore, failure to detect snowflakes / snow streak effecti…
The Hong Kong Polytechnic University（香港理工大学）；ETH Zurich（苏黎世联邦理工学院）；Center for Artificial Intelligence and Robotics, HKISI-CAS（中国科学院香港智能科学研究所人工智能与机器人中心）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
University of Science and Technology of China（中国科学技术大学）
The key to shadow removal is recovering the contents of the shadow regions with the guidance of the non-shadow regions. Due to the inadequate long-range modeling, the CNN-based approaches cannot thoroughly investigate the information from the non-shadow regions. To solve this problem, we propose a novel cleanness-navigated-shadow network (CNSNet), with a shadow-oriented adaptive normalization (SOAN) module and a shadow-aware aggregation with transformer (SAAT) module based on the shadow mask. Under the guidance of the shadow mask, the SOAN module formulates the statistics from the non-shadow r…
Queensland University of Technology（昆士兰科技大学）；The University of Sydney（悉尼大学）
A precise, controllable, interpretable and easily trainable text removal approach is necessary for both user-specific and large-scale text removal applications. To achieve this, we propose a one-stage mask-based text inpainting network, MTRNet++. It has a novel architecture that includes mask-refine, coarse-inpainting and fine-inpainting branches, and attention blocks. With this architecture, MTRNet++ can remove text either with or without an external mask. It achieves state-of-the-art results on both the Oxford and SCUT datasets without using external ground-truth masks. The results of ablati…
Moving objects in scenes are still a severe challenge for the SLAM system. Many efforts have tried to remove the motion regions in the images by detecting moving objects. In this way, the keypoints belonging to motion regions will be ignored in the later calculations. In this paper, we proposed a novel motion removal method, leveraging semantic information and optical flow to extract motion regions. Different from previous works, we don't predict moving objects or motion regions directly from image sequences. We computed rigid optical flow, synthesized by the depth and pose, and compared it ag…
AI-Powered Facial Mask Removal Is Not Suitable For Identification
Herbert Wertheim School of Optometry & Vision Science University of California, Berkeley（赫伯特·韦瑟姆视觉科学学院，加州大学伯克利分校）；School of Information University of California, Berkeley（信息学院，加州大学伯克利分校）
