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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

Focusing on your subject:Deep subject-aware image composition recommendation networks

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Computational Visual Media 2023年第1期9卷 87-107页

作者： Guo-Ye Yang Wen-Yang Zhou Yun Cai Song-Hai zhang fang-lue zhang BNRist Department of Computer Science and TechnologyTsinghua UniversityBeiing 100084China School of Engineering and Computer Science Victoria University of WellingtonWellington 6012New Zealand

Photo composition is one of the most important factors in the aesthetics of *** a popular application,composition recommendation for a photo focusing on a specific subject has been ignored by recent deep-learning-based composition recommendation *** this paper,we propose a subject-aware image composition recommendation method,SAC-Net,which takes an RGB image and a binary subject window mask as input,and returns good compositions as crops containing the *** model first determines candidate scores for all possible coarse cropping *** crops with high candidate scores are selected and further refined by regressing their corner points to generate the output recommended cropping *** final scores of the refined crops are predicted by a final score regression *** existing methods that need to preset several cropping windows,our network is able to automatically regress cropping windows with arbitrary aspect ratios and *** propose novel stability losses for maximizing smoothness when changing cropping windows along with view *** results show that our method outperforms state-of-the-art methods not only on the subject-aware image composition recommendation task,but also for general purpose composition *** also have designed a multistage labeling scheme so that a large amount of ranked pairs can be produced *** use this scheme to propose the first subject-aware composition dataset SACD,which contains 2777 images,and more than 5 million composition ranked *** SACD dataset is publicly available at https://***/SACD/.

关键词： subject-aware image composition image cropping deep learning recommendation

Sphere Face Model: A 3D morphable model with hypersphere manifold latent space using joint 2D/3D training

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Computational Visual Media 2023年第2期9卷 279-296页

作者： Diqiong Jiang Yiwei Jin fang-lue zhang Zhe Zhu Yun zhang Ruofeng Tong Min Tang Zhejiang University Hangzhou 310058China Victoria University of Wellington Wellington 6012New Zealand Duke University DurhamNorth Carolina 27708USA Communication University of Zhejiang Hangzhou 310019China

3D morphable models(3DMMs)are generative models for face shape and *** works impose face recognition constraints on 3DMM shape parameters so that the face shapes of the same person remain ***,the shape parameters of traditional 3DMMs satisfy the multivariate Gaussian *** contrast,the identity embeddings meet the hypersphere distribution,and this conflict makes it challenging for face reconstruction models to preserve the faithfulness and the shape consistency *** other words,recognition loss and reconstruction loss can not decrease jointly due to their conflict *** address this issue,we propose the Sphere Face Model(SFM),a novel 3DMM for monocular face reconstruction,preserving both shape fidelity and identity *** core of our SFM is the basis matrix which can be used to reconstruct 3D face shapes,and the basic matrix is learned by adopting a twostage training approach where 3D and 2D training data are used in the first and second stages,*** design a novel loss to resolve the distribution mismatch,enforcing that the shape parameters have the hyperspherical *** model accepts 2D and 3D data for constructing the sphere face *** experiments show that SFM has high representation ability and clustering performance in its shape parameter ***,it produces highfidelity face shapes consistently in challenging conditions in monocular face *** code will be released at https://***/a686432/SIR.

关键词： facial modeling deep learning face reconstruction 3D morphable model(3DMM)

A survey of the state-of-the-art in patch-based synthesis

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Computational Visual Media 2017年第1期3卷 3-20页

作者： Connelly Barnes fang-lue zhang University of Virginia TNList Tsinghua University

This paper surveys the state-of-the-art of research in patch-based synthesis. Patch-based methods synthesize output images by copying small regions from exemplar imagery. This line of research originated from an area called "texture synthesis", which focused on creating regular or semi-regular textures from small exemplars. However, more recently, much research has focused on synthesis of larger and more diverse imagery, such as photos, photo collections, videos, and light fields. Additionally, recent research has focused on customizing the synthesis process for particular problem domains, such as synthesizing artistic or decorative brushes, synthesis of rich materials, and synthesis for 3D fabrication. This report investigates recent papers that follow these themes, with a particular emphasis on papers published since 2009,when the last survey in this area was published. This survey can serve as a tutorial for readers who are not yet familiar with these topics, as well as provide comparisons between these papers, and highlight some open problems in this area.

关键词： texture patch image synthesis texture synthesis

JMNet: A joint matting network for automatic human matting

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Computational Visual Media 2020年第2期6卷 215-224页

作者： Xian Wu Xiao-Nan fang Tao Chen fang-lue zhang Department of Computer Science and Technology Tsinghua UniversityBeijing100084China AI Center at Visual China Group BurlingameCA94010USA School of Engineering and Computer Science Victoria University of WellingtonWellingtonNew Zealand

We propose a novel end-to-end deep learning framework, the Joint Matting Network(JMNet), to automatically generate alpha mattes for human *** utilize the intrinsic structures of the human body as seen in images by introducing a pose estimation module,which can provide both global structural guidance and a local attention focus for the matting task. Our network model includes a pose network, a trimap network, a matting network, and a shared encoder to extract features for the above three networks. We also append a trimap refinement module and utilize gradient loss to provide a sharper alpha matte. Extensive experiments have shown that our method outperforms state-of-theart human matting techniques;the shared encoder leads to better performance and lower memory *** model can process real images downloaded from the Internet for use in composition applications.

关键词： alpha matting human images deep learning pose estimation

Coherent video generation for multiple hand-held cameras with dynamic foreground

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Computational Visual Media 2020年第3期6卷 291-306页

作者： fang-lue zhang Connelly Barnes Hao-Tian zhang Junhong Zhao Gabriel Salas Victoria University of Wellington Wellington 6012New Zealand Adobe Research SeattleUSA Stanford University San FranciscoUSA

For many social events such as public performances, multiple hand-held cameras may capture the same event. This footage is often collected by amateur cinematographers who typically have little control over the scene and may not pay close attention to the camera. For these reasons, each individually captured video may fail to cover the whole time of the event, or may lose track of interesting foreground content such as a performer. We introduce a new algorithm that can synthesize a single smooth video sequence of moving foreground objects captured by multiple hand-held cameras. This allows later viewers to gain a cohesive narrative experience that can transition between different cameras, even though the input footage may be less than ideal. We first introduce a graph-based method for selecting a good transition route. This allows us to automatically select good cut points for the hand-held videos, so that smooth transitions can be created between the resulting video shots. We also propose a method to synthesize a smooth photorealistic transition video between each pair of hand-held cameras, which preserves dynamic foreground content during this transition. Our experiments demonstrate that our method outperforms previous state-of-the-art methods, which struggle to preserve dynamic foreground content.

关键词： dynamic preserve camera

VR content creation and exploration with deep learning: A survey

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Computational Visual Media 2020年第1期6卷 3-28页

作者： Miao Wang Xu-Quan Lyu Yi-Jun Li fang-lue zhang State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China. Peng Cheng Laboratory Shenzhen 518000China School of Engineering and Computer Science Victoria University of WellingtonNew Zealand

Virtual reality(VR)offers an artificial,computer generated simulation of a real life *** originated in the 1960 s and has evolved to provide increasing immersion,interactivity,imagination,and *** deep learning systems are able to represent and compose information at various levels in a deep hierarchical fashion,they can build very powerful models which leverage large quantities of visual media *** of VR methods and applications has been significantly boosted by the recent developments in deep learning *** content creation and exploration relates to image and video analysis,synthesis and editing,so deep learning methods such as fully convolutional networks and general adversarial networks are widely employed,designed specifically to handle panoramic images and video and virtual 3 D *** article surveys recent research that uses such deep learning methods for VR content creation and *** considers the problems involved,and discusses possible future directions in this active and emerging research area.

关键词： virtual reality deep learning neural networks 360°image and video virtual content

Robust tracking-by-detection using a selection and completion mechanism

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Computational Visual Media 2017年第3期3卷 285-294页

作者： Ruochen Fan fang-lue zhang Min zhang Ralph R.Martin Tsinghua University School of Engineering and Computer Science Victoria University of Wellington Center of Mathematical Sciences and Applications Harvard University School of Computer Science and Informatics Cardiff University

It is challenging to track a target continuously in videos with long-term occlusion,or objects which leave then re-enter a *** tracking algorithms combined with onlinetrained object detectors perform unreliably in complex conditions, and can only provide discontinuous trajectories with jumps in position when the object is occluded. This paper proposes a novel framework of tracking-by-detection using selection and completion to solve the abovementioned problems. It has two components, tracking and trajectory completion. An offline-trained object detector can localize objects in the same category as the object being tracked. The object detector is based on a highly accurate deep learning model. The object selector determines which object should be used to re-initialize a traditional tracker. As the object selector is trained online,it allows the framework to be adaptable. During completion, a predictive non-linear autoregressive neural network completes any discontinuous *** tracking component is an online real-time algorithm, and the completion part is an after-theevent mechanism. Quantitative experiments show a significant improvement in robustness over prior stateof-the-art methods.

关键词： object tracking detection proposal selection trajectory completion

A survey on deep geometry learning:From a representation perspective

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Computational Visual Media 2020年第2期6卷 113-133页

作者： Yun-Peng Xiao Yu-Kun Lai fang-lue zhang Chunpeng Li Lin Gao Institute of Computing Technology Chinese Academy of SciencesBeijingChina School of Computer Science and Informatics Cardiff UniversityWalesUK School of Engineering and Computer Science Victoria University of WellingtonWellingtonNew Zealand

Researchers have achieved great success in dealing with 2 D images using deep *** recent years,3 D computer vision and geometry deep learning have gained ever more *** advanced techniques for 3 D shapes have been proposed for different *** 2 D images,which can be uniformly represented by a regular grid of pixels,3 D shapes have various representations,such as depth images,multi-view images,voxels,point clouds,meshes,implicit surfaces,*** performance achieved in different applications largely depends on the representation used,and there is no unique representation that works well for all ***,in this survey,we review recent developments in deep learning for 3 D geometry from a representation perspective,summarizing the advantages and disadvantages of different representations for different *** also present existing datasets in these representations and further discuss future research directions.

关键词： 3D shape representation geometry learning neural networks computer graphics