Graphical Models最新文献_第6页

MixNet: Mix different networks for learning 3D implicit representations MixNet:混合不同的网络来学习3D隐式表示

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2023-10-01 DOI: 10.1016/j.gmod.2023.101190

Bowen Lyu , Li-Yong Shen , Chun-Ming Yuan

引用次数: 0

Fast progressive polygonal approximations for online strokes 快速渐进多边形近似在线笔画

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2023-10-01 DOI: 10.1016/j.gmod.2023.101200

Mohammad Tanvir Parvez

引用次数: 1

Unified shape and appearance reconstruction with joint camera parameter refinement 联合摄像机参数细化统一形状和外观重建

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2023-10-01 DOI: 10.1016/j.gmod.2023.101193

Julian Kaltheuner, Patrick Stotko, Reinhard Klein

{"title":"Unified shape and appearance reconstruction with joint camera parameter refinement","authors":"Julian Kaltheuner, Patrick Stotko, Reinhard Klein","doi":"10.1016/j.gmod.2023.101193","DOIUrl":"10.1016/j.gmod.2023.101193","url":null,"abstract":"<div><p>In this paper, we present an inverse rendering method for the simple reconstruction of shape and appearance of real-world objects from only roughly calibrated RGB images captured under collocated point light illumination. To this end, we gradually reconstruct the lower-frequency geometry information using automatically generated occupancy mask images based on a visual hull initialization of the mesh, to infer the object topology, and a smoothness-preconditioned optimization. By combining this geometry estimation with learning-based SVBRDF parameter inference as well as intrinsic and extrinsic camera parameter refinement in a joint and unified formulation, our novel method is able to reconstruct shape and an isotropic SVBRDF from fewer input images than previous methods. Unlike in other works, we also estimate normal maps as part of the SVBRDF to capture and represent higher-frequency geometric details in a compact way. Furthermore, by regularizing the appearance estimation with a GAN-based SVBRDF generator, we are able to meaningfully limit the solution space. In summary, this leads to a robust automatic reconstruction algorithm for shape and appearance. We evaluated our algorithm on synthetic as well as on real-world data and demonstrate that our method is able to reconstruct complex objects with high-fidelity reflection properties in a robust way, also in the presence of imperfect camera parameter data.</p></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"129 ","pages":"Article 101193"},"PeriodicalIF":1.7,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43340086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised learning of style-aware facial animation from real acting performances 从真实表演中无监督地学习风格感知面部动画

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2023-10-01 DOI: 10.1016/j.gmod.2023.101199

Wolfgang Paier , Anna Hilsmann , Peter Eisert

{"title":"Unsupervised learning of style-aware facial animation from real acting performances","authors":"Wolfgang Paier , Anna Hilsmann , Peter Eisert","doi":"10.1016/j.gmod.2023.101199","DOIUrl":"https://doi.org/10.1016/j.gmod.2023.101199","url":null,"abstract":"<div><p>This paper presents a novel approach for text/speech-driven animation of a photo-realistic head model based on blend-shape geometry, dynamic textures, and neural rendering. Training a VAE for geometry and texture yields a parametric model for accurate capturing and realistic synthesis of facial expressions from a latent feature vector. Our animation method is based on a conditional CNN that transforms text or speech into a sequence of animation parameters. In contrast to previous approaches, our animation model learns disentangling/synthesizing different acting-styles in an unsupervised manner, requiring only phonetic labels that describe the content of training sequences. For realistic real-time rendering, we train a U-Net that refines rasterization-based renderings by computing improved pixel colors and a foreground matte. We compare our framework qualitatively/quantitatively against recent methods for head modeling as well as facial animation and evaluate the perceived rendering/animation quality in a user-study, which indicates large improvements compared to state-of-the-art approaches.</p></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"129 ","pages":"Article 101199"},"PeriodicalIF":1.7,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49889738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint data and feature augmentation for self-supervised representation learning on point clouds 点云上自监督表示学习的联合数据和特征增强

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2023-10-01 DOI: 10.1016/j.gmod.2023.101188

Zhuheng Lu , Yuewei Dai , Weiqing Li , Zhiyong Su

{"title":"Joint data and feature augmentation for self-supervised representation learning on point clouds","authors":"Zhuheng Lu , Yuewei Dai , Weiqing Li , Zhiyong Su","doi":"10.1016/j.gmod.2023.101188","DOIUrl":"https://doi.org/10.1016/j.gmod.2023.101188","url":null,"abstract":"<div><p>To deal with the exhausting annotations, self-supervised representation learning from unlabeled point clouds has drawn much attention, especially centered on augmentation-based contrastive methods. However, specific augmentations hardly produce sufficient transferability to high-level tasks on different datasets. Besides, augmentations on point clouds may also change underlying semantics. To address the issues, we propose a simple but efficient augmentation fusion contrastive learning framework to combine data augmentations in Euclidean space and feature augmentations in feature space. In particular, we propose a data augmentation method based on sampling and graph generation. Meanwhile, we design a data augmentation network to enable a correspondence of representations by maximizing consistency between augmented graph pairs. We further design a feature augmentation network that encourages the model to learn representations invariant to the perturbations using an encoder perturbation. We comprehensively conduct extensive object classification experiments and object part segmentation experiments to validate the transferability of the proposed framework. Experimental results demonstrate that the proposed framework is effective to learn the point cloud representation in a self-supervised manner, and yields state-of-the-art results in the community. The source code is publicly available at: <span>https://github.com/VCG-NJUST/AFSRL</span><svg><path></path></svg>.</p></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"129 ","pages":"Article 101188"},"PeriodicalIF":1.7,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49889741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Realistic simulation of fruit mildew diseases: Skin discoloration, fungus growth and volume shrinkage 水果霉菌病的真实模拟：皮肤变色、真菌生长和体积缩小

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2023-10-01 DOI: 10.1016/j.gmod.2023.101194

Yixin Xu , Shiguang Liu

{"title":"Realistic simulation of fruit mildew diseases: Skin discoloration, fungus growth and volume shrinkage","authors":"Yixin Xu , Shiguang Liu","doi":"10.1016/j.gmod.2023.101194","DOIUrl":"10.1016/j.gmod.2023.101194","url":null,"abstract":"<div><p>Time-varying effects simulation plays a critical role in computer graphics. Fruit diseases are typical time-varying phenomena. Due to the biological complexity, the existing methods fail to represent the biodiversity and biological law of symptoms. To this end, this paper proposes a biology-aware, physically-based framework that respects biological knowledge for realistic simulation of fruit mildew diseases. The simulated symptoms include skin discoloration, fungus growth, and volume shrinkage. Specifically, we take advantage of both the zero-order kinetic model and reaction–diffusion model to represent the complex fruit skin discoloration related to skin biological characteristics. To reproduce 3D mildew growth, we employ the Poisson-disk sampling technique and propose a template model instancing method. One can flexibly change hyphal template models to characterize the fungal biological diversity. To model the fruit’s biological structure, we fill the fruit mesh interior with particles in a biologically-based arrangement. Based on this structure, we propose a turgor pressure and a Lennard-Jones force-based adaptive mass–spring system to simulate the fruit shrinkage in a biological manner. Experiments verified that the proposed framework can effectively simulate mildew diseases, including gray mold, powdery mildew, and downy mildew. Our results are visually compelling and close to the ground truth. Both quantitative and qualitative experiments validated the proposed method.</p></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"129 ","pages":"Article 101194"},"PeriodicalIF":1.7,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41392551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Component-aware generative autoencoder for structure hybrid and shape completion 面向结构混合和形状补全的组件感知生成式自编码器

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2023-10-01 DOI: 10.1016/j.gmod.2023.101185

Fan Zhang, Qiang Fu, Yang Liu, Xueming Li

{"title":"Component-aware generative autoencoder for structure hybrid and shape completion","authors":"Fan Zhang, Qiang Fu, Yang Liu, Xueming Li","doi":"10.1016/j.gmod.2023.101185","DOIUrl":"https://doi.org/10.1016/j.gmod.2023.101185","url":null,"abstract":"<div><p>Assembling components of man-made objects to create new structures or complete 3D shapes is a popular approach in 3D modeling techniques. Recently, leveraging deep neural networks for assembly-based 3D modeling has been widely studied. However, exploring new component combinations even across different categories is still challenging for most of the deep-learning-based 3D modeling methods. In this paper, we propose a novel generative autoencoder that tackles the component combinations for 3D modeling of man-made objects. We use the segmented input objects to create component volumes that have redundant components and random configurations. By using the input objects and the associated component volumes to train the autoencoder, we can obtain an object volume consisting of components with proper quality and structure as the network output. Such a generative autoencoder can be applied to either multiple object categories for structure hybrid or a single object category for shape completion. We conduct a series of evaluations and experimental results to demonstrate the usability and practicability of our method.</p></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"129 ","pages":"Article 101185"},"PeriodicalIF":1.7,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49889740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-homogeneous denoising for virtual reality in real-time path tracing rendering 实时路径跟踪绘制中虚拟现实的非均匀去噪

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2023-10-01 DOI: 10.1016/j.gmod.2023.101184

Victor Peres , Esteban Clua , Thiago Porcino , Anselmo Montenegro

{"title":"Non-homogeneous denoising for virtual reality in real-time path tracing rendering","authors":"Victor Peres , Esteban Clua , Thiago Porcino , Anselmo Montenegro","doi":"10.1016/j.gmod.2023.101184","DOIUrl":"10.1016/j.gmod.2023.101184","url":null,"abstract":"<div><p>Real time Path-tracing is becoming an important approach for the future of games, digital entertainment, and virtual reality applications that require realism and immersive environments. Among different possible optimizations, denoising Monte Carlo rendered images is necessary in low sampling densities. When dealing with Virtual Reality devices, other possibilities can also be considered, such as foveated rendering techniques. Hence, this work proposes a novel and promising rendering pipeline for denoising a real-time path-traced application in a dual-screen system such as head-mounted display (HMD) devices. Therefore, we leverage characteristics of the foveal vision by computing G-Buffers with the features of the scene and a buffer with the foveated distribution for both left and right screens. Later, we path trace the image within the coordinates buffer generating only a few initial rays per selected pixel, and reconstruct the noisy image output with a novel non-homogeneous denoiser that accounts for the pixel distribution. Our experiments showed that this proposed rendering pipeline could achieve a speedup factor up to 1.35 compared to one without our optimizations.</p></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"129 ","pages":"Article 101184"},"PeriodicalIF":1.7,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43863090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Obituary: Christoph M. Hoffmann 讣告:Christoph M. Hoffmann

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2023-10-01 DOI: 10.1016/j.gmod.2023.101186

引用次数: 0

ImplicitPCA: Implicitly-proxied parametric encoding for collision-aware garment reconstruction 用于碰撞感知服装重构的隐式代理参数编码

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2023-10-01 DOI: 10.1016/j.gmod.2023.101195

Lan Chen , Jie Yang , Hongbo Fu , Xiaoxu Meng , Weikai Chen , Bo Yang , Lin Gao

{"title":"ImplicitPCA: Implicitly-proxied parametric encoding for collision-aware garment reconstruction","authors":"Lan Chen , Jie Yang , Hongbo Fu , Xiaoxu Meng , Weikai Chen , Bo Yang , Lin Gao","doi":"10.1016/j.gmod.2023.101195","DOIUrl":"https://doi.org/10.1016/j.gmod.2023.101195","url":null,"abstract":"<div><p>The emerging remote collaboration in a virtual environment calls for quickly generating high-fidelity 3D humans with cloth from a single image. To estimate clothing geometry and topology, parametric models are widely used but often lack details. Alternative approaches based on implicit functions can generate accurate details but are limited to closed surfaces and may not produce physically correct reconstructions, such as collision-free human avatars. To solve these problems, we present <em>ImplicitPCA</em>, a framework for high-fidelity single-view garment reconstruction that bridges the good ends of explicit and implicit representations. The key is a parametric SDF network that closely couples parametric encoding with implicit functions and thus enjoys the fine details brought by implicit reconstruction while maintaining correct topology with open surfaces. We further introduce a collision-aware regression network to ensure the physical correctness of cloth and human. During inference, an iterative routine is applied to an input image with 2D garment landmarks to obtain optimal parameters by aligning the cloth mesh projection with the 2D landmarks and fitting the parametric implicit fields with the reconstructed cloth SDF. The experiments on the public dataset and in-the-wild images demonstrate that our result outperforms the prior works, reconstructing detailed, topology-correct 3D garments while avoiding garment-body collisions.</p></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"129 ","pages":"Article 101195"},"PeriodicalIF":1.7,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49889737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0