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Generating animatable 3D cartoon faces from single portraits

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Virtual Reality & Intelligent Hardware 2024年第4期6卷 292-307页

作者： Chuanyu PAN Guowei YANG Taijiang MU yu-kun lai Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA 94704 USA Department of Computer Science and Technology Tsinghua University Beijing 100084 China School of Computer Science and Informatics Cardiff University Cardiff CF24 4AG UK

Background With the development of virtual reality (VR) technology, there is a growing need for customized 3D avatars. However, traditional methods for 3D avatar modeling are either time-consuming or fail to retain the similarity to the person being modeled. This study presents a novel framework for generating animatable 3D cartoon faces from a single portrait image. Methods First, we transferred an input real-world portrait to a stylized cartoon image using StyleGAN. We then proposed a two-stage reconstruction method to recover a 3D cartoon face with detailed texture. Our two-stage strategy initially performs coarse estimation based on template models and subsequently refines the model by nonrigid deformation under landmark supervision. Finally, we proposed a semantic-preserving face-rigging method based on manually created templates and deformation transfer. Conclusions Compared with prior arts, the qualitative and quantitative results show that our method achieves better accuracy, aesthetics, and similarity criteria. Furthermore, we demonstrated the capability of the proposed 3D model for real-time facial animation.

关键词： 3D reconstruction Cartoon face reconstruction Face rigging Stylized reconstruction Virtual reality

STATE:Learning structure and texture representations for novel view synthesis

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Computational Visual Media 2023年第4期9卷 767-786页

作者： Xinyi Jing Qiao Feng yu-kun lai Jinsong Zhang yuanqiang yu kun Li College of Intelligence and Computing Tianjin UniversityTianjin 300350China School of Computer Science and Informatics Cardiff UniversityCardiff CF244AGUK

Novel viewpoint image synthesis is very challenging,especially from sparse views,due to large changes in viewpoint and *** image-based methods fail to generate reasonable results for invisible regions,while geometry-based methods have difficulties in synthesizing detailed *** this paper,we propose STATE,an end-to-end deep neural network,for sparse view synthesis by learning structure and texture *** is encoded as a hybrid feature field to predict reasonable structures for invisible regions while maintaining original structures for visible regions,and texture is encoded as a deformed feature map to preserve detailed *** propose a hierarchical fusion scheme with intra-branch and inter-branch aggregation,in which spatio-view attention allows multi-view fusion at the feature level to adaptively select important information by regressing pixel-wise or voxel-wise confidence *** decoding the aggregated features,STATE is able to generate realistic images with reasonable structures and detailed *** results demonstrate that our method achieves qualitatively and quantitatively better results than state-of-the-art *** method also enables texture and structure editing applications benefiting from implicit disentanglement of structure and *** code is available at http://***/faculty/likun/projects/STATE.

关键词： novel view synthesis sparse views spatioview attention structure representation texture representation

MILI:Multi-person inference from a low-resolution image

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Fundamental Research 2023年第3期3卷 434-441页

作者： kun Li yunke Liu yu-kun lai Jingyu Yang Tianjin University Tianjin 300350China CardiffUniversity CardiffCF244AGUnited Kingdom

Existing multi-person reconstruction methods require the human bodies in the input image to occupy a considerable portion of the ***,low-resolution human objects are ubiquitous due to trade-offbetween the field of view and target distance given a limited camera *** this paper,we propose an end-to-end multi-task framework for multi-person inference from a low-resolution image(MILI).To perceive more information from a low-resolution image,we use pair-wise images at high resolution and low resolution for training,and design a restoration network with a simple loss for better feature extraction from the low-resolution *** address the occlusion problem in multi-person scenes,we propose an occlusion-aware mask prediction network to estimate the mask of each person during 3D mesh *** results on both small-scale scenes and large-scale scenes demonstrate that our method outperforms the state-of-the-art methods both quantitatively and *** code is available at http://***/faculty/likun/projects/MILI.

关键词： Multi-person reconstruction Low-resolution human objects End-to-end Multi-task learning Occlusion-aware prediction

3D corrective nose reconstruction from a single image

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Computational Visual Media 2022年第2期8卷 225-237页

作者： Yanlong Tang yun Zhang Xiaoguang Han Fang-Lue Zhang yu-kun lai Ruofeng Tong Tencent Games Lightspeed&Quantum Studios ShenzhenChina Communication University of Zhejiang HangzhouChina Shenzhen Research Institute of Big Data the Chinese University of Hong Kong(Shenzhen)ShenzhenChina Victoria University of Wellington WellingtonNew Zealand Cardiff University WalesUK Zhejiang University HangzhouChina

There is a steadily growing range of applications that can benefit from facial reconstruction techniques,leading to an increasing demand for reconstruction of high-quality 3D face *** it is an important expressive part of the human face,the nose has received less attention than other expressive regions in the face reconstruction *** applying existing reconstruction methods to facial images,the reconstructed nose models are often inconsistent with the desired shape and *** this paper,we propose a coarse-to-fine 3D nose reconstruction and correction pipeline to build a nose model from a single image,where 3D and 2D nose curve correspondences are adaptively updated and *** first correct the reconstruction result coarsely using constraints of 3D-2D sparse landmark correspondences,and then heuristically update a dense 3D-2D curve correspondence based on the coarsely corrected result.A final refinement step is performed to correct the shape based on the updated 3D-2D dense curve *** results show the advantages of our method for 3D nose reconstruction over existing methods.

关键词： nose shape recovery single image 3D reconstruction contour correspondence Laplacian deformation

Lesion region segmentation via weakly supervised learning

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Quantitative Biology 2022年第3期10卷 239-252页

作者： Ran Yi Rui Zeng Yang Weng Minjing yu yu-kun lai Yong-Jin Liu Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China College of Intelligence and Computing Tianjin UniversityTianjin 300350China School of Computer Science and Informatics Cardiff UniversityCardiffCF103ATUnited Kingdom

Background:Image-based automatic diagnosis of field diseases can help increase crop yields and is of great ***,crop lesion regions tend to be scattered and of varying sizes,this along with substantial intraclass variation and small inter-class variation makes segmentation ***:We propose a novel end-to-end system that only requires weak supervision of image-level labels for lesion region ***,a two-branch network is designed for joint disease classification and seed region *** generated seed regions are then used as input to the next segmentation stage where we design to use an encoder-decoder *** from previous works that use an encoder in the segmentation network,the encoder-decoder network is critical for our system to successfully segment images with small and scattered regions,which is the major challenge in image-based diagnosis of field *** further propose a novel weakly supervised training strategy for the encoder-decoder semantic segmentation network,making use of the extracted seed ***:Experimental results show that our system achieves better lesion region segmentation results than state of the *** addition to crop images,our method is also applicable to general scattered object *** demonstrate this by extending our framework to work on the PASCAL VOC dataset,which achieves comparable performance with the state-of-the-art DSRG(deep seeded region growing)***:Our method not only outperforms state-of-the-art semantic segmentation methods by a large margin for the lesion segmentation task,but also shows its capability to perform well on more general tasks.

关键词： weakly supervised learning lesion segmentation disease detection semantic segmentation agriculture

A review of image and video colorization: From analogies to deep learning

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Visual Informatics 2022年第3期6卷 51-68页

作者： Shu-yu Chen Jia-Qi Zhang You-You Zhao Paul L.Rosin yu-kun lai Lin Gao Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing TechnologyChinese Academy of SciencesBeijingChina Beihang University BeijingChina University of California Santa CruzCAUS Cardiff University WalesUK University of Chinese Academy of Sciences BeijingChina

Image colorization is a classic and important topic in computer graphics,where the aim is to add color to a monochromatic input image to produce a colorful *** this survey,we present the history of colorization research in chronological order and summarize popular algorithms in this *** work on colorization mostly focused on developing techniques to improve the colorization *** the last few years,researchers have considered more possibilities such as combining colorization with NLP(natural language processing)and focused more on industrial *** better control the color,various types of color control are designed,such as providing reference images or *** have created a taxonomy of the colorization methods according to the input type,divided into grayscale,sketch-based and *** pros and cons are discussed for each algorithm,and they are compared according to their main ***,we discuss how deep learning,and in particular Generative Adversarial Networks(GANs),has changed this field.

关键词： Image colorization Sketch colorization Manga colorization

A Revisit of Shape Editing Techniques:From the Geometric to the Neural Viewpoint

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Journal of Computer Science & Technology 2021年第3期36卷 520-554页

作者： yu-Jie yuan yu-kun lai Tong Wu Lin Gao Ligang Liu Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China School of Computer Science and Informatics Cardiff UniversityCardiff CF243AAU.K. School of Mathematical Sciences University of Science and Technology of ChinaHefei 230026China

3D shape editing is widely used in a range of applications such as movie production,computer games and computer aided *** is also a popular research topic in computer graphics and computer *** past decades,researchers have developed a series of editing methods to make the editing process faster,more robust,and more ***,the deformed shape is determined by the optimal transformation and weights for an energy *** increasing availability of 3D shapes on the Internet,data-driven methods were proposed to improve the editing *** recently as the deep neural networks became popular,many deep learning based editing methods have been developed in this field,which are naturally *** mainly survey recent research studies from the geometric viewpoint to those emerging neural deformation techniques and categorize them into organic shape editing methods and man-made model editing *** traditional methods and recent neural network based methods are reviewed.

关键词： mesh deformation man-made model editing deformation representation optimization deep learning

NPRportrait 1.0:A three-level benchmark for non-photorealistic rendering of portraits

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Computational Visual Media 2022年第3期8卷 445-465页

作者： Paul L.Rosin yu-kun lai David Mould Ran Yi Itamar Berger Lars Doyle Seungyong Lee Chuan Li Yong-Jin Liu Amir Semmo Ariel Shamir Minjung Son Holger Winnemöller School of Computer Science and Informatics Cardiff UniversityCardiffUK School of Computer Science Carleton UniversityOttawaCanada Department of Computer Science and Technology Tsinghua UniversityBeijingChina Reichman University(the Interdisciplinary Center) HerzliyaIsrael Department of Computer Science and Engineering Pohang University of Science and TechnologyPohangRepublic of Korea Lambda Labs Inc.San FranciscoUSA Hasso Plattner Institute University of PotsdamPotsdamGermany Multimedia Processing Laboratory Samsung Advanced Institute of TechnologySuwonRepublic of Korea Adobe Systems Inc.San JoseUSA

Recently,there has been an upsurge of activity in image-based non-photorealistic rendering(NPR),and in particular portrait image stylisation,due to the advent of neural style transfer(NST).However,the state of performance evaluation in this field is poor,especially compared to the norms in the computer vision and machine learning ***,the task of evaluating image stylisation is thus far not well defined,since it involves subjective,perceptual,and aesthetic *** make progress towards a solution,this paper proposes a new structured,threelevel,benchmark dataset for the evaluation of stylised portrait *** criteria were used for its construction,and its consistency was validated by user ***,a new methodology has been developed for evaluating portrait stylisation algorithms,which makes use of the different benchmark levels as well as annotations provided by user studies regarding the characteristics of the *** perform evaluation for a wide variety of image stylisation methods(both portrait-specific and general purpose,and also both traditional NPR approaches and NST)using the new benchmark dataset.

关键词： non-photorealistic rendering(NPR) image stylization style transfer portrait evaluation benchmark

Saliency guided local and global descriptors for effective action recognition

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Computational Visual Media 2016年第1期2卷 97-106页

作者： Ashwan Abdulmunem yu-kun lai Xianfang Sun School of Computer Science and Informatics Cardiff University Department of Computer Science School of ScienceKerbala University

This paper presents a novel framework for human action recognition based on salient object detection and a new combination of local and global descriptors. We first detect salient objects in video frames and only extract features for such *** then use a simple strategy to identify and process only those video frames that contain salient objects. Processing salient objects instead of all frames not only makes the algorithm more efficient, but more importantly also suppresses the interference of background pixels. We combine this approach with a new combination of local and global descriptors, namely3D-SIFT and histograms of oriented optical flow(HOOF), respectively. The resulting saliency guided3D-SIFT–HOOF(SGSH) feature is used along with a multi-class support vector machine(SVM) classifier for human action recognition. Experiments conducted on the standard KTH and UCF-Sports action benchmarks show that our new method outperforms the competing state-of-the-art spatiotemporal feature-based human action recognition methods.

关键词： action recognition saliency detection local and global descriptors bag of visual words(Bo VWs) classification