Semantic face editing of real world facial images is an important application of generative models. Recently, multiple works have explored possible techniques to generate such modifications using the latent structure of pre-trained GAN models. However, such approaches often require training an encoder network and that is typically a time-consuming and resource intensive process. A possible alternative to such a GAN-based architecture can be styleALAE, a latent-space based autoencoder that can generate photo-realistic images of high quality. Unfortunately, the reconstructed image in styleALAE does not preserve the identity of the input facial image. This limits the application of styleALAE for semantic face editing of images with known identities. In our work, we use a recent advancement in one-shot domain adaptation to address this problem. Our work ensures that the identity of the reconstructed image is the same as the given input image. We further generate semantic modifications over the reconstructed image by using the latent space of the pre-trained styleALAE model. Results show that our approach can generate semantic modifications on any real world facial image while preserving the identity.
Zhejiang University（浙江大学）；University of California at Merced（加州大学默塞德分校）；NEC Laboratories America（NEC美国实验室）
A caricature is an artistic form of a person's picture in which certain striking characteristics are abstracted or exaggerated in order to create a humor or sarcasm effect. For numerous caricature related applications such as attribute recognition and caricature editing, face parsing is an essential pre-processing step that provides a complete facial structure understanding. However, current state-of-the-art face parsing methods require large amounts of labeled data on the pixel-level and such process for caricature is tedious and labor-intensive. For real photos, there are numerous labeled da…
Low-Cost Test-Time Adaptation for Robust Video Editing
UESTC（电子科技大学）；USTC（中国科学技术大学）；University of Minnesota–Twin Cities（明尼苏达大学-双城分校）；Emory University（埃默里大学）；Nanjing University（南京大学）；Lanzhou University（兰州大学）；Ohio State University, Columbus（俄亥俄州立大学-哥伦布分校）；Chinese Academy of Sciences（中国科学院）；Peking University（北京大学）
GAN Inversion for Image Editing via Unsupervised Domain Adaptation
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Virginia（弗吉尼亚大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；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）
Automatically learned quality assessment for images has recently become a hot topic due to its usefulness in a wide variety of applications such as evaluating image capture pipelines, storage techniques and sharing media. Despite the subjective nature of this problem, most existing methods only predict the mean opinion score provided by datasets such as AVA [1] and TID2013 [2]. Our approach differs from others in that we predict the distribution of human opinion scores using a convolutional neural network. Our architecture also has the advantage of being significantly simpler than other method…
CPAM: Context-Preserving Adaptive Manipulation for Zero-Shot Real Image Editing
Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam（越南科学大学信息科技学院）；Vietnam National University, Ho Chi Minh City, Vietnam（越南国家大学）；Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia（莫纳什大学信息科技学院）；Department of Computer Science, University of Dayton, Dayton, Ohio, US（Dayton 大学计算机科学系）
DIVE: Taming DINO for Subject-Driven Video Editing
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；vivo AI Lab（vivo AI实验室）；NVIDIA；Adobe Research（Adobe研究院）；Shenzhen University（深圳大学）；Southeast University（东南大学）；Shenzhen University of Advanced Technology（深圳大学先进技术学院）
GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing
School of Computing and Information Systems, The University of Melbourne, Australia（墨尔本大学计算与信息系统学院）；Mohamed bin Zayed University of Artificial Intelligence, UAE（人工智能 Mohamed bin Zayed 大学）；Information Technology University, Punjab, Pakistan（巴基斯坦旁遮普信息科技大学）
Fashion attribute editing is a task that aims to convert the semantic attributes of a given fashion image while preserving the irrelevant regions. Previous works typically employ conditional GANs where the generator explicitly learns the target attributes and directly execute the conversion. These approaches, however, are neither scalable nor generic as they operate only with few limited attributes and a separate generator is required for each dataset or attribute set. Inspired by the recent advancement of diffusion models, we explore the classifier-guided diffusion that leverages the off-the-…
