arXiv - CS - Graphics最新文献_第9页

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":null,"pages":null},"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

Fine-grained Metrics for Point Cloud Semantic Segmentation 点云语义分割的细粒度指标

arXiv - CS - Graphics Pub Date : 2024-07-31 DOI: arxiv-2407.21289

Zhuheng Lu, Ting Wu, Yuewei Dai, Weiqing Li, Zhiyong Su

引用次数: 0

A Comparative Study of Neural Surface Reconstruction for Scientific Visualization 用于科学可视化的神经表面重构比较研究

arXiv - CS - Graphics Pub Date : 2024-07-30 DOI: arxiv-2407.20868

Siyuan Yao, Weixi Song, Chaoli Wang

引用次数: 0

Monocular Human-Object Reconstruction in the Wild 野外单目人-物重构

arXiv - CS - Graphics Pub Date : 2024-07-30 DOI: arxiv-2407.20566

Chaofan Huo, Ye Shi, Jingya Wang

{"title":"Monocular Human-Object Reconstruction in the Wild","authors":"Chaofan Huo, Ye Shi, Jingya Wang","doi":"arxiv-2407.20566","DOIUrl":"https://doi.org/arxiv-2407.20566","url":null,"abstract":"Learning the prior knowledge of the 3D human-object spatial relation is\u0000crucial for reconstructing human-object interaction from images and\u0000understanding how humans interact with objects in 3D space. Previous works\u0000learn this prior from datasets collected in controlled environments, but due to\u0000the diversity of domains, they struggle to generalize to real-world scenarios.\u0000To overcome this limitation, we present a 2D-supervised method that learns the\u00003D human-object spatial relation prior purely from 2D images in the wild. Our\u0000method utilizes a flow-based neural network to learn the prior distribution of\u0000the 2D human-object keypoint layout and viewports for each image in the\u0000dataset. The effectiveness of the prior learned from 2D images is demonstrated\u0000on the human-object reconstruction task by applying the prior to tune the\u0000relative pose between the human and the object during the post-optimization\u0000stage. To validate and benchmark our method on in-the-wild images, we collect\u0000the WildHOI dataset from the YouTube website, which consists of various\u0000interactions with 8 objects in real-world scenarios. We conduct the experiments\u0000on the indoor BEHAVE dataset and the outdoor WildHOI dataset. The results show\u0000that our method achieves almost comparable performance with fully 3D supervised\u0000methods on the BEHAVE dataset, even if we have only utilized the 2D layout\u0000information, and outperforms previous methods in terms of generality and\u0000interaction diversity on in-the-wild images.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141863845","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

StackFLOW: Monocular Human-Object Reconstruction by Stacked Normalizing Flow with Offset StackFLOW：通过带偏移的叠加归一化流进行单目人-物重构

arXiv - CS - Graphics Pub Date : 2024-07-30 DOI: arxiv-2407.20545

Chaofan Huo, Ye Shi, Yuexin Ma, Lan Xu, Jingyi Yu, Jingya Wang

{"title":"StackFLOW: Monocular Human-Object Reconstruction by Stacked Normalizing Flow with Offset","authors":"Chaofan Huo, Ye Shi, Yuexin Ma, Lan Xu, Jingyi Yu, Jingya Wang","doi":"arxiv-2407.20545","DOIUrl":"https://doi.org/arxiv-2407.20545","url":null,"abstract":"Modeling and capturing the 3D spatial arrangement of the human and the object\u0000is the key to perceiving 3D human-object interaction from monocular images. In\u0000this work, we propose to use the Human-Object Offset between anchors which are\u0000densely sampled from the surface of human mesh and object mesh to represent\u0000human-object spatial relation. Compared with previous works which use contact\u0000map or implicit distance filed to encode 3D human-object spatial relations, our\u0000method is a simple and efficient way to encode the highly detailed spatial\u0000correlation between the human and object. Based on this representation, we\u0000propose Stacked Normalizing Flow (StackFLOW) to infer the posterior\u0000distribution of human-object spatial relations from the image. During the\u0000optimization stage, we finetune the human body pose and object 6D pose by\u0000maximizing the likelihood of samples based on this posterior distribution and\u0000minimizing the 2D-3D corresponding reprojection loss. Extensive experimental\u0000results show that our method achieves impressive results on two challenging\u0000benchmarks, BEHAVE and InterCap datasets.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141863847","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

colorspace: A Python Toolbox for Manipulating and Assessing Colors and Palettes 颜色空间用于操作和评估颜色与调色板的 Python 工具箱

arXiv - CS - Graphics Pub Date : 2024-07-29 DOI: arxiv-2407.19921

Reto Stauffer, Achim Zeileis

引用次数: 0

Structure-Aware Simplification for Hypergraph Visualization 超图可视化的结构感知简化

arXiv - CS - Graphics Pub Date : 2024-07-29 DOI: arxiv-2407.19621

Peter Oliver, Eugene Zhang, Yue Zhang

{"title":"Structure-Aware Simplification for Hypergraph Visualization","authors":"Peter Oliver, Eugene Zhang, Yue Zhang","doi":"arxiv-2407.19621","DOIUrl":"https://doi.org/arxiv-2407.19621","url":null,"abstract":"Hypergraphs provide a natural way to represent polyadic relationships in\u0000network data. For large hypergraphs, it is often difficult to visually detect\u0000structures within the data. Recently, a scalable polygon-based visualization\u0000approach was developed allowing hypergraphs with thousands of hyperedges to be\u0000simplified and examined at different levels of detail. However, this approach\u0000is not guaranteed to eliminate all of the visual clutter caused by unavoidable\u0000overlaps. Furthermore, meaningful structures can be lost at simplified scales,\u0000making their interpretation unreliable. In this paper, we define hypergraph\u0000structures using the bipartite graph representation, allowing us to decompose\u0000the hypergraph into a union of structures including topological blocks,\u0000bridges, and branches, and to identify exactly where unavoidable overlaps must\u0000occur. We also introduce a set of topology preserving and topology altering\u0000atomic operations, enabling the preservation of important structures while\u0000reducing unavoidable overlaps to improve visual clarity and interpretability in\u0000simplified scales. We demonstrate our approach in several real-world\u0000applications.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141863851","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

From Flat to Spatial: Comparison of 4 methods constructing 3D, 2 and 1/2D Models from 2D Plans with neural networks 从平面到空间：利用神经网络从二维平面图构建三维、二维和 1/2D 模型的四种方法比较

arXiv - CS - Graphics Pub Date : 2024-07-29 DOI: arxiv-2407.19970

Jacob Sam, Karan Patel, Mike Saad

{"title":"From Flat to Spatial: Comparison of 4 methods constructing 3D, 2 and 1/2D Models from 2D Plans with neural networks","authors":"Jacob Sam, Karan Patel, Mike Saad","doi":"arxiv-2407.19970","DOIUrl":"https://doi.org/arxiv-2407.19970","url":null,"abstract":"In the field of architecture, the conversion of single images into 2 and 1/2D\u0000and 3D meshes is a promising technology that enhances design visualization and\u0000efficiency. This paper evaluates four innovative methods: \"One-2-3-45,\" \"CRM:\u0000Single Image to 3D Textured Mesh with Convolutional Reconstruction Model,\"\u0000\"Instant Mesh,\" and \"Image-to-Mesh.\" These methods are at the forefront of this\u0000technology, focusing on their applicability in architectural design and\u0000visualization. They streamline the creation of 3D architectural models,\u0000enabling rapid prototyping and detailed visualization from minimal initial\u0000inputs, such as photographs or simple sketches.One-2-3-45 leverages a\u0000diffusion-based approach to generate multi-view reconstructions, ensuring high\u0000geometric fidelity and texture quality. CRM utilizes a convolutional network to\u0000integrate geometric priors into its architecture, producing detailed and\u0000textured meshes quickly and efficiently. Instant Mesh combines the strengths of\u0000multi-view diffusion and sparse-view models to offer speed and scalability,\u0000suitable for diverse architectural projects. Image-to-Mesh leverages a\u0000generative adversarial network (GAN) to produce 3D meshes from single images,\u0000focusing on maintaining high texture fidelity and geometric accuracy by\u0000incorporating image and depth map data into its training process. It uses a\u0000hybrid approach that combines voxel-based representations with surface\u0000reconstruction techniques to ensure detailed and realistic 3D models.This\u0000comparative study highlights each method's contribution to reducing design\u0000cycle times, improving accuracy, and enabling flexible adaptations to various\u0000architectural styles and requirements. By providing architects with powerful\u0000tools for rapid visualization and iteration, these advancements in 3D mesh\u0000generation are set to revolutionize architectural practices.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141863849","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

Physically-based Path Tracer using WebGPU and OpenPBR 使用 WebGPU 和 OpenPBR 的物理路径追踪器

arXiv - CS - Graphics Pub Date : 2024-07-29 DOI: arxiv-2407.19977

Simon Stucki, Philipp Ackermann

引用次数: 0

textsc{Perm}: A Parametric Representation for Multi-Style 3D Hair Modeling textsc{Perm}：多风格三维发型建模的参数表示法

arXiv - CS - Graphics Pub Date : 2024-07-28 DOI: arxiv-2407.19451

Chengan He, Xin Sun, Zhixin Shu, Fujun Luan, Sören Pirk, Jorge Alejandro Amador Herrera, Dominik L. Michels, Tuanfeng Y. Wang, Meng Zhang, Holly Rushmeier, Yi Zhou

{"title":"textsc{Perm}: A Parametric Representation for Multi-Style 3D Hair Modeling","authors":"Chengan He, Xin Sun, Zhixin Shu, Fujun Luan, Sören Pirk, Jorge Alejandro Amador Herrera, Dominik L. Michels, Tuanfeng Y. Wang, Meng Zhang, Holly Rushmeier, Yi Zhou","doi":"arxiv-2407.19451","DOIUrl":"https://doi.org/arxiv-2407.19451","url":null,"abstract":"We present textsc{Perm}, a learned parametric model of human 3D hair\u0000designed to facilitate various hair-related applications. Unlike previous work\u0000that jointly models the global hair shape and local strand details, we propose\u0000to disentangle them using a PCA-based strand representation in the frequency\u0000domain, thereby allowing more precise editing and output control. Specifically,\u0000we leverage our strand representation to fit and decompose hair geometry\u0000textures into low- to high-frequency hair structures. These decomposed textures\u0000are later parameterized with different generative models, emulating common\u0000stages in the hair modeling process. We conduct extensive experiments to\u0000validate the architecture design of textsc{Perm}, and finally deploy the\u0000trained model as a generic prior to solve task-agnostic problems, further\u0000showcasing its flexibility and superiority in tasks such as 3D hair\u0000parameterization, hairstyle interpolation, single-view hair reconstruction, and\u0000hair-conditioned image generation. Our code and data will be available at:\u0000url{https://github.com/c-he/perm}.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141863984","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