IEEE Computer Graphics and Applications最新文献

Critical Interactivity: Exploration and Narration in Data Visualization. 关键的互动性：数据可视化中的探索和叙述。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-04-08 DOI: 10.1109/MCG.2025.3544684

Francesca Morini, Manuela Garreton, Jona Pomerance, Nadia Zeissig, Sabine de Guenther, Fidel Thomet, Linda Freyberg, Ilias Kyriazis, Andrea Scholz, Marian Dork

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引用次数: 0

MarsIPAN: Optimization and Negotiations in Mars Sample Return Scheduling Coordination. MarsIPAN：火星样品返回调度协调的优化与协商。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-04-07 DOI: 10.1109/MCG.2025.3558426

Jasmine T Otto, Malika Khurana, Noah Deutsch, Benjamin P S Donitz, Oskar Elek, Scott Davidoff

引用次数: 0

Computational Design and Fabrication of Protective Foam. 防护泡沫的计算设计与制造。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-04-01 DOI: 10.1109/MCG.2025.3556656

Tsukasa Fukusato, Naoki Kita

引用次数: 0

Unified Visual Comparison Framework for Human and AI Paintings using Neural Embeddings and Computational Aesthetics. 使用神经嵌入和计算美学的人类和人工智能绘画的统一视觉比较框架。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-04-01 DOI: 10.1109/MCG.2025.3555122

Yilin Ye, Rong Huang, Kang Zhang, Wei Zeng

引用次数: 0

The UDV Card Deck: A Collaborative Design Framework to Facilitate Urban Visualization Conversations. UDV卡组：促进城市可视化对话的协作设计框架。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-03-31 DOI: 10.1109/MCG.2025.3556573

Damla Cay, Till Nagel, Sebastian Meier

引用次数: 0

Meet-In-Style: Text-driven Real-time Video Stylization using Diffusion Models. 风格相遇：使用扩散模型的文本驱动的实时视频风格化。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-03-24 DOI: 10.1109/MCG.2025.3554312

David Kunz, Ondrej Texler, David Mould, Daniel Sykora

引用次数: 0

Voting-Based Intervention Planning Using AI-Generated Images. 使用人工智能生成的图像进行基于投票的干预计划。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-03-21 DOI: 10.1109/MCG.2025.3553620

Ioannis Kavouras, Ioannis Rallis, Emmanuel Sardis, Anastasios Doulamis, Nikolaos Doulamis

引用次数: 0

HoloJig: Interactive Spoken Prompt Specified Generative AI Environments. HoloJig：交互式语音提示指定生成AI环境。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-03-21 DOI: 10.1109/MCG.2025.3553780

Llogari Casas, Samantha Hannah, Kenny Mitchell

引用次数: 0

Should I render or should AI Generate? Crafting Synthetic Semantic Segmentation Datasets with Controlled Generation. 我应该渲染还是AI生成？合成语义分割数据集与控制生成。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-03-21 DOI: 10.1109/MCG.2025.3553494

Omar A Mures, Manuel Silva, Manuel Lijo-Sanchez, Emilio J Padron, Jose A Iglesias-Guitian

{"title":"Should I render or should AI Generate? Crafting Synthetic Semantic Segmentation Datasets with Controlled Generation.","authors":"Omar A Mures, Manuel Silva, Manuel Lijo-Sanchez, Emilio J Padron, Jose A Iglesias-Guitian","doi":"10.1109/MCG.2025.3553494","DOIUrl":"10.1109/MCG.2025.3553494","url":null,"abstract":"This work explores the integration of generative AI models for automatically generating synthetic image-labeled data. Our approach leverages controllable Diffusion Models to generate synthetic variations of semantically labeled images. Synthetic datasets for semantic segmentation struggle to represent real-world subtleties, such as different weather conditions or fine details, typically relying on costly simulations and rendering. However, Diffusion Models can generate diverse images using input text prompts and guidance images, like semantic masks. Our work introduces and tests a novel methodology for generating labeled synthetic images, with an initial focus on semantic segmentation, a demanding computer vision task. We showcase our approach in two distinct image segmentation domains, outperforming traditional computer graphics simulations in efficiently creating diverse datasets and training downstream models. We leverage generative models for crafting synthetically labeled images, posing the question: \"Should I render or should AI generate?\". Our results endorse a paradigm shift towards controlled generation models.","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"PP ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674963","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

What Data Does and Does Not Represent: Visualizing the Archive of Slavery. 数据能代表什么，不能代表什么：将奴隶制档案可视化。

IF 1.7 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-03-20 DOI: 10.1109/MCG.2025.3553412

Shiyao Li, Margy Adams, Tanvi Sharma, Jay Varner, Lauren Klein

引用次数: 0