arXiv - CS - Graphics最新文献_第5页

2DGH: 2D Gaussian-Hermite Splatting for High-quality Rendering and Better Geometry Reconstruction 2DGH：二维高斯-赫米特拼接技术实现高质量渲染和更好的几何重构

arXiv - CS - Graphics Pub Date : 2024-08-30 DOI: arxiv-2408.16982

Ruihan Yu, Tianyu Huang, Jingwang Ling, Feng Xu

引用次数: 0

Adaptive Multi-Resolution Encoding for Interactive Large-Scale Volume Visualization through Functional Approximation 通过函数逼近实现自适应多分辨率编码，实现交互式大规模体积可视化

arXiv - CS - Graphics Pub Date : 2024-08-30 DOI: arxiv-2409.00184

Jianxin Sun, David Lenz, Hongfeng Yu, Tom Peterka

{"title":"Adaptive Multi-Resolution Encoding for Interactive Large-Scale Volume Visualization through Functional Approximation","authors":"Jianxin Sun, David Lenz, Hongfeng Yu, Tom Peterka","doi":"arxiv-2409.00184","DOIUrl":"https://doi.org/arxiv-2409.00184","url":null,"abstract":"Functional approximation as a high-order continuous representation provides a\u0000more accurate value and gradient query compared to the traditional discrete\u0000volume representation. Volume visualization directly rendered from functional\u0000approximation generates high-quality rendering results without high-order\u0000artifacts caused by trilinear interpolations. However, querying an encoded\u0000functional approximation is computationally expensive, especially when the\u0000input dataset is large, making functional approximation impractical for\u0000interactive visualization. In this paper, we proposed a novel functional\u0000approximation multi-resolution representation, Adaptive-FAM, which is\u0000lightweight and fast to query. We also design a GPU-accelerated out-of-core\u0000multi-resolution volume visualization framework that directly utilizes the\u0000Adaptive-FAM representation to generate high-quality rendering with interactive\u0000responsiveness. Our method can not only dramatically decrease the caching time,\u0000one of the main contributors to input latency, but also effectively improve the\u0000cache hit rate through prefetching. Our approach significantly outperforms the\u0000traditional function approximation method in terms of input latency while\u0000maintaining comparable rendering quality.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221878","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

RenDetNet: Weakly-supervised Shadow Detection with Shadow Caster Verification RenDetNet：弱监督阴影检测与阴影捕捉器验证

arXiv - CS - Graphics Pub Date : 2024-08-30 DOI: arxiv-2408.17143

Nikolina Kubiak, Elliot Wortman, Armin Mustafa, Graeme Phillipson, Stephen Jolly, Simon Hadfield

引用次数: 0

ReconX: Reconstruct Any Scene from Sparse Views with Video Diffusion Model ReconX：利用视频扩散模型从稀疏视图重建任何场景

arXiv - CS - Graphics Pub Date : 2024-08-29 DOI: arxiv-2408.16767

Fangfu Liu, Wenqiang Sun, Hanyang Wang, Yikai Wang, Haowen Sun, Junliang Ye, Jun Zhang, Yueqi Duan

{"title":"ReconX: Reconstruct Any Scene from Sparse Views with Video Diffusion Model","authors":"Fangfu Liu, Wenqiang Sun, Hanyang Wang, Yikai Wang, Haowen Sun, Junliang Ye, Jun Zhang, Yueqi Duan","doi":"arxiv-2408.16767","DOIUrl":"https://doi.org/arxiv-2408.16767","url":null,"abstract":"Advancements in 3D scene reconstruction have transformed 2D images from the\u0000real world into 3D models, producing realistic 3D results from hundreds of\u0000input photos. Despite great success in dense-view reconstruction scenarios,\u0000rendering a detailed scene from insufficient captured views is still an\u0000ill-posed optimization problem, often resulting in artifacts and distortions in\u0000unseen areas. In this paper, we propose ReconX, a novel 3D scene reconstruction\u0000paradigm that reframes the ambiguous reconstruction challenge as a temporal\u0000generation task. The key insight is to unleash the strong generative prior of\u0000large pre-trained video diffusion models for sparse-view reconstruction.\u0000However, 3D view consistency struggles to be accurately preserved in directly\u0000generated video frames from pre-trained models. To address this, given limited\u0000input views, the proposed ReconX first constructs a global point cloud and\u0000encodes it into a contextual space as the 3D structure condition. Guided by the\u0000condition, the video diffusion model then synthesizes video frames that are\u0000both detail-preserved and exhibit a high degree of 3D consistency, ensuring the\u0000coherence of the scene from various perspectives. Finally, we recover the 3D\u0000scene from the generated video through a confidence-aware 3D Gaussian Splatting\u0000optimization scheme. Extensive experiments on various real-world datasets show\u0000the superiority of our ReconX over state-of-the-art methods in terms of quality\u0000and generalizability.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221888","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

Advancing Architectural Floorplan Design with Geometry-enhanced Graph Diffusion 利用几何增强图形扩散推进建筑平面图设计

arXiv - CS - Graphics Pub Date : 2024-08-29 DOI: arxiv-2408.16258

Sizhe Hu, Wenming Wu, Yuntao Wang, Benzhu Xu, Liping Zheng

{"title":"Advancing Architectural Floorplan Design with Geometry-enhanced Graph Diffusion","authors":"Sizhe Hu, Wenming Wu, Yuntao Wang, Benzhu Xu, Liping Zheng","doi":"arxiv-2408.16258","DOIUrl":"https://doi.org/arxiv-2408.16258","url":null,"abstract":"Automating architectural floorplan design is vital for housing and interior\u0000design, offering a faster, cost-effective alternative to manual sketches by\u0000architects. However, existing methods, including rule-based and learning-based\u0000approaches, face challenges in design complexity and constrained generation\u0000with extensive post-processing, and tend to obvious geometric inconsistencies\u0000such as misalignment, overlap, and gaps. In this work, we propose a novel\u0000generative framework for vector floorplan design via structural graph\u0000generation, called GSDiff, focusing on wall junction generation and wall\u0000segment prediction to capture both geometric and semantic aspects of structural\u0000graphs. To improve the geometric rationality of generated structural graphs, we\u0000propose two innovative geometry enhancement methods. In wall junction\u0000generation, we propose a novel alignment loss function to improve geometric\u0000consistency. In wall segment prediction, we propose a random self-supervision\u0000method to enhance the model's perception of the overall geometric structure,\u0000thereby promoting the generation of reasonable geometric structures. Employing\u0000the diffusion model and the Transformer model, as well as the geometry\u0000enhancement strategies, our framework can generate wall junctions, wall\u0000segments and room polygons with structural and semantic information, resulting\u0000in structural graphs that accurately represent floorplans. Extensive\u0000experiments show that the proposed method surpasses existing techniques,\u0000enabling free generation and constrained generation, marking a shift towards\u0000structure generation in architectural design.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221886","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

UV-free Texture Generation with Denoising and Geodesic Heat Diffusions 利用去噪和大地热扩散技术生成无紫外线纹理

arXiv - CS - Graphics Pub Date : 2024-08-29 DOI: arxiv-2408.16762

Simone Foti, Stefanos Zafeiriou, Tolga Birdal

引用次数: 0

G-Style: Stylized Gaussian Splatting G-Style：风格化高斯溅射

arXiv - CS - Graphics Pub Date : 2024-08-28 DOI: arxiv-2408.15695

Áron Samuel Kovács, Pedro Hermosilla, Renata G. Raidou

{"title":"G-Style: Stylized Gaussian Splatting","authors":"Áron Samuel Kovács, Pedro Hermosilla, Renata G. Raidou","doi":"arxiv-2408.15695","DOIUrl":"https://doi.org/arxiv-2408.15695","url":null,"abstract":"We introduce G-Style, a novel algorithm designed to transfer the style of an\u0000image onto a 3D scene represented using Gaussian Splatting. Gaussian Splatting\u0000is a powerful 3D representation for novel view synthesis, as -- compared to\u0000other approaches based on Neural Radiance Fields -- it provides fast scene\u0000renderings and user control over the scene. Recent pre-prints have demonstrated\u0000that the style of Gaussian Splatting scenes can be modified using an image\u0000exemplar. However, since the scene geometry remains fixed during the\u0000stylization process, current solutions fall short of producing satisfactory\u0000results. Our algorithm aims to address these limitations by following a\u0000three-step process: In a pre-processing step, we remove undesirable Gaussians\u0000with large projection areas or highly elongated shapes. Subsequently, we\u0000combine several losses carefully designed to preserve different scales of the\u0000style in the image, while maintaining as much as possible the integrity of the\u0000original scene content. During the stylization process and following the\u0000original design of Gaussian Splatting, we split Gaussians where additional\u0000detail is necessary within our scene by tracking the gradient of the stylized\u0000color. Our experiments demonstrate that G-Style generates high-quality\u0000stylizations within just a few minutes, outperforming existing methods both\u0000qualitatively and quantitatively.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221912","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

Micro and macro facial expressions by driven animations in realistic Virtual Humans 逼真虚拟人中的微观和宏观面部表情驱动动画

arXiv - CS - Graphics Pub Date : 2024-08-28 DOI: arxiv-2408.16110

Rubens Halbig Montanha, Giovana Nascimento Raupp, Ana Carolina Policarpo Schmitt, Victor Flávio de Andrade Araujo, Soraia Raupp Musse

{"title":"Micro and macro facial expressions by driven animations in realistic Virtual Humans","authors":"Rubens Halbig Montanha, Giovana Nascimento Raupp, Ana Carolina Policarpo Schmitt, Victor Flávio de Andrade Araujo, Soraia Raupp Musse","doi":"arxiv-2408.16110","DOIUrl":"https://doi.org/arxiv-2408.16110","url":null,"abstract":"Computer Graphics (CG) advancements have allowed the creation of more\u0000realistic Virtual Humans (VH) through modern techniques for animating the VH\u0000body and face, thereby affecting perception. From traditional methods,\u0000including blend shapes, to driven animations using facial and body tracking,\u0000these advancements can potentially enhance the perception of comfort and\u0000realism in relation to VHs. Previously, Psychology studied facial movements in\u0000humans, with some works separating expressions into macro and micro\u0000expressions. Also, some previous CG studies have analyzed how macro and micro\u0000expressions are perceived, replicating psychology studies in VHs, encompassing\u0000studies with realistic and cartoon VHs, and exploring different VH\u0000technologies. However, instead of using facial tracking animation methods,\u0000these previous studies animated the VHs using blendshapes interpolation. To\u0000understand how the facial tracking technique alters the perception of VHs, this\u0000paper extends the study to macro and micro expressions, employing two datasets\u0000to transfer real facial expressions to VHs and analyze how their expressions\u0000are perceived. Our findings suggest that transferring facial expressions from\u0000real actors to VHs significantly diminishes the accuracy of emotion perception\u0000compared to VH facial animations created by artists.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221887","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

Evaluating and Comparing Crowd Simulations: Perspectives from a Crowd Authoring Tool 评估和比较人群模拟：来自人群创作工具的视角

arXiv - CS - Graphics Pub Date : 2024-08-28 DOI: arxiv-2408.15762

Gabriel Fonseca Silva, Paulo Ricardo Knob, Rubens Halbig Montanha, Soraia Raupp Musse

引用次数: 0

OctFusion: Octree-based Diffusion Models for 3D Shape Generation OctFusion：基于八维扩散模型的三维形状生成

arXiv - CS - Graphics Pub Date : 2024-08-27 DOI: arxiv-2408.14732

Bojun Xiong, Si-Tong Wei, Xin-Yang Zheng, Yan-Pei Cao, Zhouhui Lian, Peng-Shuai Wang

{"title":"OctFusion: Octree-based Diffusion Models for 3D Shape Generation","authors":"Bojun Xiong, Si-Tong Wei, Xin-Yang Zheng, Yan-Pei Cao, Zhouhui Lian, Peng-Shuai Wang","doi":"arxiv-2408.14732","DOIUrl":"https://doi.org/arxiv-2408.14732","url":null,"abstract":"Diffusion models have emerged as a popular method for 3D generation. However,\u0000it is still challenging for diffusion models to efficiently generate diverse\u0000and high-quality 3D shapes. In this paper, we introduce OctFusion, which can\u0000generate 3D shapes with arbitrary resolutions in 2.5 seconds on a single Nvidia\u00004090 GPU, and the extracted meshes are guaranteed to be continuous and\u0000manifold. The key components of OctFusion are the octree-based latent\u0000representation and the accompanying diffusion models. The representation\u0000combines the benefits of both implicit neural representations and explicit\u0000spatial octrees and is learned with an octree-based variational autoencoder.\u0000The proposed diffusion model is a unified multi-scale U-Net that enables\u0000weights and computation sharing across different octree levels and avoids the\u0000complexity of widely used cascaded diffusion schemes. We verify the\u0000effectiveness of OctFusion on the ShapeNet and Objaverse datasets and achieve\u0000state-of-the-art performances on shape generation tasks. We demonstrate that\u0000OctFusion is extendable and flexible by generating high-quality color fields\u0000for textured mesh generation and high-quality 3D shapes conditioned on text\u0000prompts, sketches, or category labels. Our code and pre-trained models are\u0000available at url{https://github.com/octree-nn/octfusion}.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221915","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