arXiv - CS - Graphics最新文献_第9页

Demystifying Spatial Dependence: Interactive Visualizations for Interpreting Local Spatial Autocorrelation 揭开空间相关性的神秘面纱：解读局部空间自相关性的互动可视化方法

arXiv - CS - Graphics Pub Date : 2024-08-05 DOI: arxiv-2408.02418

Lee Mason, Blanaid Hicks, Jonas Almeida

引用次数: 0

MeshAnything V2: Artist-Created Mesh Generation With Adjacent Mesh Tokenization MeshAnything V2：通过相邻网格标记化生成艺术家创作的网格

arXiv - CS - Graphics Pub Date : 2024-08-05 DOI: arxiv-2408.02555

Yiwen Chen, Yikai Wang, Yihao Luo, Zhengyi Wang, Zilong Chen, Jun Zhu, Chi Zhang, Guosheng Lin

引用次数: 0

TurboEdit: Text-Based Image Editing Using Few-Step Diffusion Models TurboEdit：使用几步扩散模型进行基于文本的图像编辑

arXiv - CS - Graphics Pub Date : 2024-08-01 DOI: arxiv-2408.00735

Gilad Deutch, Rinon Gal, Daniel Garibi, Or Patashnik, Daniel Cohen-Or

引用次数: 0

MotionFix: Text-Driven 3D Human Motion Editing MotionFix：文本驱动的 3D 人体动作编辑

arXiv - CS - Graphics Pub Date : 2024-08-01 DOI: arxiv-2408.00712

Nikos Athanasiou, Alpár Ceske, Markos Diomataris, Michael J. Black, Gül Varol

{"title":"MotionFix: Text-Driven 3D Human Motion Editing","authors":"Nikos Athanasiou, Alpár Ceske, Markos Diomataris, Michael J. Black, Gül Varol","doi":"arxiv-2408.00712","DOIUrl":"https://doi.org/arxiv-2408.00712","url":null,"abstract":"The focus of this paper is 3D motion editing. Given a 3D human motion and a\u0000textual description of the desired modification, our goal is to generate an\u0000edited motion as described by the text. The challenges include the lack of\u0000training data and the design of a model that faithfully edits the source\u0000motion. In this paper, we address both these challenges. We build a methodology\u0000to semi-automatically collect a dataset of triplets in the form of (i) a source\u0000motion, (ii) a target motion, and (iii) an edit text, and create the new\u0000MotionFix dataset. Having access to such data allows us to train a conditional\u0000diffusion model, TMED, that takes both the source motion and the edit text as\u0000input. We further build various baselines trained only on text-motion pairs\u0000datasets, and show superior performance of our model trained on triplets. We\u0000introduce new retrieval-based metrics for motion editing and establish a new\u0000benchmark on the evaluation set of MotionFix. Our results are encouraging,\u0000paving the way for further research on finegrained motion generation. Code and\u0000models will be made publicly available.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141885797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SF3D: Stable Fast 3D Mesh Reconstruction with UV-unwrapping and Illumination Disentanglement SF3D：利用 UV 解包和照明解缠实现稳定的快速 3D 网格重构

arXiv - CS - Graphics Pub Date : 2024-08-01 DOI: arxiv-2408.00653

Mark Boss, Zixuan Huang, Aaryaman Vasishta, Varun Jampani

引用次数: 0

Neural Octahedral Field: Octahedral prior for simultaneous smoothing and sharp edge regularization 神经八面体场：同时进行平滑和锐边正则化的八面体先验

arXiv - CS - Graphics Pub Date : 2024-08-01 DOI: arxiv-2408.00303

Ruichen Zheng, Tao Yu

{"title":"Neural Octahedral Field: Octahedral prior for simultaneous smoothing and sharp edge regularization","authors":"Ruichen Zheng, Tao Yu","doi":"arxiv-2408.00303","DOIUrl":"https://doi.org/arxiv-2408.00303","url":null,"abstract":"Neural implicit representation, the parameterization of distance function as\u0000a coordinate neural field, has emerged as a promising lead in tackling surface\u0000reconstruction from unoriented point clouds. To enforce consistent orientation,\u0000existing methods focus on regularizing the gradient of the distance function,\u0000such as constraining it to be of the unit norm, minimizing its divergence, or\u0000aligning it with the eigenvector of Hessian that corresponds to zero\u0000eigenvalue. However, under the presence of large scanning noise, they tend to\u0000either overfit the noise input or produce an excessively smooth reconstruction.\u0000In this work, we propose to guide the surface reconstruction under a new\u0000variant of neural field, the octahedral field, leveraging the spherical\u0000harmonics representation of octahedral frames originated in the hexahedral\u0000meshing. Such field automatically snaps to geometry features when constrained\u0000to be smooth, and naturally preserves sharp angles when interpolated over\u0000creases. By simultaneously fitting and smoothing the octahedral field alongside\u0000the implicit geometry, it behaves analogously to bilateral filtering, resulting\u0000in smooth reconstruction while preserving sharp edges. Despite being operated\u0000purely pointwise, our method outperforms various traditional and neural\u0000approaches across extensive experiments, and is very competitive with methods\u0000that require normal and data priors. Our full implementation is available at:\u0000https://github.com/Ankbzpx/frame-field.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"218 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141885804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reenact Anything: Semantic Video Motion Transfer Using Motion-Textual Inversion 重现一切：利用运动文本反转实现语义视频运动转移

arXiv - CS - Graphics Pub Date : 2024-08-01 DOI: arxiv-2408.00458

Manuel Kansy, Jacek Naruniec, Christopher Schroers, Markus Gross, Romann M. Weber

{"title":"Reenact Anything: Semantic Video Motion Transfer Using Motion-Textual Inversion","authors":"Manuel Kansy, Jacek Naruniec, Christopher Schroers, Markus Gross, Romann M. Weber","doi":"arxiv-2408.00458","DOIUrl":"https://doi.org/arxiv-2408.00458","url":null,"abstract":"Recent years have seen a tremendous improvement in the quality of video\u0000generation and editing approaches. While several techniques focus on editing\u0000appearance, few address motion. Current approaches using text, trajectories, or\u0000bounding boxes are limited to simple motions, so we specify motions with a\u0000single motion reference video instead. We further propose to use a pre-trained\u0000image-to-video model rather than a text-to-video model. This approach allows us\u0000to preserve the exact appearance and position of a target object or scene and\u0000helps disentangle appearance from motion. Our method, called motion-textual\u0000inversion, leverages our observation that image-to-video models extract\u0000appearance mainly from the (latent) image input, while the text/image embedding\u0000injected via cross-attention predominantly controls motion. We thus represent\u0000motion using text/image embedding tokens. By operating on an inflated\u0000motion-text embedding containing multiple text/image embedding tokens per\u0000frame, we achieve a high temporal motion granularity. Once optimized on the\u0000motion reference video, this embedding can be applied to various target images\u0000to generate videos with semantically similar motions. Our approach does not\u0000require spatial alignment between the motion reference video and target image,\u0000generalizes across various domains, and can be applied to various tasks such as\u0000full-body and face reenactment, as well as controlling the motion of inanimate\u0000objects and the camera. We empirically demonstrate the effectiveness of our\u0000method in the semantic video motion transfer task, significantly outperforming\u0000existing methods in this context.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141885798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

StyleRF-VolVis: Style Transfer of Neural Radiance Fields for Expressive Volume Visualization StyleRF-VolVis：神经辐射场的风格转移，实现富有表现力的体量可视化

arXiv - CS - Graphics Pub Date : 2024-07-31 DOI: arxiv-2408.00150

Kaiyuan Tang, Chaoli Wang

{"title":"StyleRF-VolVis: Style Transfer of Neural Radiance Fields for Expressive Volume Visualization","authors":"Kaiyuan Tang, Chaoli Wang","doi":"arxiv-2408.00150","DOIUrl":"https://doi.org/arxiv-2408.00150","url":null,"abstract":"In volume visualization, visualization synthesis has attracted much attention\u0000due to its ability to generate novel visualizations without following the\u0000conventional rendering pipeline. However, existing solutions based on\u0000generative adversarial networks often require many training images and take\u0000significant training time. Still, issues such as low quality, consistency, and\u0000flexibility persist. This paper introduces StyleRF-VolVis, an innovative style\u0000transfer framework for expressive volume visualization (VolVis) via neural\u0000radiance field (NeRF). The expressiveness of StyleRF-VolVis is upheld by its\u0000ability to accurately separate the underlying scene geometry (i.e., content)\u0000and color appearance (i.e., style), conveniently modify color, opacity, and\u0000lighting of the original rendering while maintaining visual content consistency\u0000across the views, and effectively transfer arbitrary styles from reference\u0000images to the reconstructed 3D scene. To achieve these, we design a base NeRF\u0000model for scene geometry extraction, a palette color network to classify\u0000regions of the radiance field for photorealistic editing, and an unrestricted\u0000color network to lift the color palette constraint via knowledge distillation\u0000for non-photorealistic editing. We demonstrate the superior quality,\u0000consistency, and flexibility of StyleRF-VolVis by experimenting with various\u0000volume rendering scenes and reference images and comparing StyleRF-VolVis\u0000against other image-based (AdaIN), video-based (ReReVST), and NeRF-based (ARF\u0000and SNeRF) style rendering solutions.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141885795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Seamless Parametrization in Penner Coordinates 彭纳坐标中的无缝参数化

arXiv - CS - Graphics Pub Date : 2024-07-31 DOI: arxiv-2407.21342

Ryan Capouellez, Denis Zorin

引用次数: 0

Deformable 3D Shape Diffusion Model 可变形三维形状扩散模型

arXiv - CS - Graphics Pub Date : 2024-07-31 DOI: arxiv-2407.21428

Dengsheng Chen, Jie Hu, Xiaoming Wei, Enhua Wu

{"title":"Deformable 3D Shape Diffusion Model","authors":"Dengsheng Chen, Jie Hu, Xiaoming Wei, Enhua Wu","doi":"arxiv-2407.21428","DOIUrl":"https://doi.org/arxiv-2407.21428","url":null,"abstract":"The Gaussian diffusion model, initially designed for image generation, has\u0000recently been adapted for 3D point cloud generation. However, these adaptations\u0000have not fully considered the intrinsic geometric characteristics of 3D shapes,\u0000thereby constraining the diffusion model's potential for 3D shape manipulation.\u0000To address this limitation, we introduce a novel deformable 3D shape diffusion\u0000model that facilitates comprehensive 3D shape manipulation, including point\u0000cloud generation, mesh deformation, and facial animation. Our approach\u0000innovatively incorporates a differential deformation kernel, which deconstructs\u0000the generation of geometric structures into successive non-rigid deformation\u0000stages. By leveraging a probabilistic diffusion model to simulate this\u0000step-by-step process, our method provides a versatile and efficient solution\u0000for a wide range of applications, spanning from graphics rendering to facial\u0000expression animation. Empirical evidence highlights the effectiveness of our\u0000approach, demonstrating state-of-the-art performance in point cloud generation\u0000and competitive results in mesh deformation. Additionally, extensive visual\u0000demonstrations reveal the significant potential of our approach for practical\u0000applications. Our method presents a unique pathway for advancing 3D shape\u0000manipulation and unlocking new opportunities in the realm of virtual reality.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141863854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0